Applied AI Consulting

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Operation Conduction

Applied AI Consulting (AAIC) developed a Bookmark Feature aimed at resolving these issues. This involved:

Creating advanced filters that allow users to narrow searches by parameters such as location, industry, revenue, headcount, and more.
Enabling users to save filtered data as bookmarks with custom names and comments.
Providing a centralized vault to store and view bookmarks in a tabular format.
Implementing tracking functionality to monitor updates to bookmarked data, including changes in employee roles or company status.
Introducing collaboration tools like comment sections and notifications for shared bookmarks.

Approach to the Solution

Requirement Analysis

Identified key pain points like data overwhelm, lack of organization, and inefficient team collaboration.

Feature Development

Developed advanced filters for precise data retrieval.
Built a bookmarking system with customization options and centralized storage.
Integrated real-time tracking for monitoring updates to saved data.

Collaboration Tools

Added comment functionality for team discussions within bookmarks.
Designed notification systems to alert users about comments or updates on shared bookmarks.

User-Centric Design

Ensured the feature was intuitive and easy to adopt for both tech-savvy and non-technical users.

Business Impact

Improved Efficiency

Customers could focus on specific datasets, saving significant time compared to manually filtering and re-searching data repeatedly.

Enhanced Collaboration

Teams could share bookmarks and comments seamlessly, improving communication and reducing redundant efforts.

Data Organization

The centralized vault offered a streamlined way to manage, revisit, and utilize purchased data over time.

Real-Time Updates

Users received updated and accurate information, ensuring the data remained relevant and actionable.

Business Growth

Clients using the feature reported increased productivity and improved decision-making capabilities, leading to greater satisfaction and retention.

Long-Term Scalability

The feature’s flexible design allows for easy adaptation to other industries or domains requiring data management solutions.
It supports integration with future enhancements, such as AI-based suggestions for bookmarks or predictive analytics for filtered data.
The bookmark vault can scale to accommodate growing datasets as the client base and data volume expand.
Cross-platform compatibility ensures the feature remains accessible across devices and environments.

Real-World Impact

One of AAIC’s standout innovations is the Bookmark Feature, designed to help businesses efficiently manage large datasets. By enabling precise filtering, central storage, real-time updates, and seamless collaboration, this feature exemplifies AAIC’s ability to translate customer challenges into transformative technological solutions.

Conclusion

The Bookmark Feature transformed the way users managed large datasets, turning data chaos into organized, actionable insights. By addressing key challenges and introducing a user-friendly, collaborative, and scalable solution, this feature significantly enhanced the customer experience while driving business value for the platform. Its success showcases the potential for leveraging technology to solve complex data management issues efficiently.

Transform Your Business with Applied AI Consulting

Schedule a consultation today to explore how our AI-driven solutions can unlock new opportunities for your organization. Book a meeting at Calendly.com/aaic.

Customer Testimonials

Switching to GitHub Actions has been a game-changer for our CI/CD workflows. The migration process was smooth, and the reduction in operational overhead and security risks has significantly improved our development efficiency. We can now focus on delivering value without worrying about complex infrastructure management.
Head of QA Engineer

Unlocking the Power of a True Blog Writer for Your Business

Wed, 04 Dec 2024 17:02:58 +0000

In today’s digital landscape, creating engaging content is essential for any business. But how do you ensure that your blog resonates with your audience? Enter the True Blog Writer -an AI-powered tool that crafts personalized blog content tailored to your unique business needs.

This innovative solution not only understands your offerings but also connects with your target audience, making it a game-changer for your content strategy. Let’s dive into how a personalized blog writer can transform your blogging efforts.

Discover the True Blog Writer: Your New Best Friend

What is a True Blog Writer?

A True Blog Writer is an advanced AI tool designed to generate blog content that is specifically tailored to your business. Unlike traditional writing methods, it uses data and insights from your website to create content that truly resonates with your audience.

This means you can say goodbye to generic posts that fail to engage. Instead, you get content that speaks directly to your readers.

How It Understands Your Company and Offerings

So, how does it work? This AI agent analyzes your company’s offerings, values, and target demographics from your website and other data sources. It generates content that reflects your brand’s voice and message.

Imagine having a writing assistant that knows your business inside and out. That’s the power of a True Blog Writer.

The Benefits of a Personalized Blog Writer

Tailored Content for Your Target Audience

One of the standout features of a personalized blog writer is its ability to create content specifically tailored to your audience’s interests and needs. This targeted approach increases engagement and relevance, making your blog a go-to resource for your readers.

Have you ever wondered why some blogs get more interaction than others? The answer often lies in how well they understand their audience.

Leveraging YOUR Case Studies for Authenticity

Unlike chatGPT and other blog writing softwares, another significant advantage is the ability to incorporate examples from your case studies. By doing this, the AI creates authentic and relatable content that showcases your expertise and success stories.

This not only builds trust with your audience but also highlights your achievements in a compelling way.

How a True Blog Writer Works

The Technology Behind Personalized Blog Writing

Utilizing advanced algorithms and machine learning, a True Blog Writer analyzes vast amounts of data. It confirms the authenticity of the data from you before proceeding. This helps it understand trends and preferences in your industry, ensuring that your content is always relevant.

Curious about how it generates content? Let’s break it down.

Step-by-Step Process of Generating a Blog

Input your company website.
Blog Brief
Targeted funnel stage : Awareness/Consideration/Decision making
Primary and secondary keyword you want to focus on

The Blog writers studies your website, understands case studies, and creates almost ready to publish blog. It’s that simple! You can have a blog post ready in no time. We recommend you to add images manually for better ranking.

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Click here

](mailto:sanju@appliedaiconsulting.com?subject=Get%20your%20first%204%20blogs%20free!)

Real-World Examples: Success Stories with a Personalized Blog Writer

Case Study 1: Boosting Engagement

A marketing agency decided to use a True Blog Writer to create targeted content. The result? A remarkable 35% increase in reader engagement within just three months.

Isn’t that impressive? It shows how effective personalized content can be.

Case Study 2: Driving Conversions

Another example comes from a digital marketing firm that saw a 23% increase in conversions. They achieved this by implementing personalized blog posts that addressed specific customer pain points.

This demonstrates the tangible benefits of using a personalized blog writer.

Getting Started with Your True Blog Writer

Setting Up Your Account

Ready to take the plunge? To get started, send an email and AAIC will help create an account. Follow the prompts to input your company information and preferences.

It’s a straightforward process that opens the door to a world of personalized content.

Tips for Maximizing Your Blog’s Impact

Regularly update your case studies (Our upcoming AI Agent will help here as well)
Use analytics to track engagement and adjust content strategies accordingly.

These tips will help you get the most out of your True Blog Writer.

Frequently Asked Questions About Personalized Blog Writers

What Makes a Blog Truly Personalized?

A truly personalized blog considers the unique aspects of your business. This includes your voice, audience, and specific case studies that highlight your strengths. It studies your website, LinkedIn page in detail to understand you before writing the first word.

How Does It Compare to Traditional Blog Writing?

Unlike traditional methods, a True Blog Writer uses data-driven insights. This creates content that is more relevant and engaging for your audience.

Take Action: Transform Your Blogging Strategy Today

Why You Should Invest in a True Blog Writer

Investing in a True Blog Writer can save you time and enhance content quality. Ultimately, it leads to better engagement and conversions.

Achieve explosive growth using AI Agents!

Next Steps for Implementation

Start by exploring the features of Marxeed. Consider how it can fit into your content strategy.

In conclusion, a True Blog Writer is not just a tool; it’s a partner in your content creation journey. By understanding your business and audience, it helps you craft personalized content that drives results.

So, why wait? Sign up for Marxeed today for free! You can start creating personalized blog content in just a few minutes. Experience the benefits of a personalized blog writer and watch your engagement soar.

OpsPilot: Autonomous Password Manager Fixes All Password Issues

Wed, 04 Dec 2024 16:50:35 +0000

In today’s digital landscape, managing passwords can feel like a daunting task. With countless accounts requiring unique passwords, it’s easy to become overwhelmed. Fortunately, automated password fixing is here to simplify the process.

Imagine a world where you can change your password in just five minutes, without any human intervention. That’s exactly what OpsPilot offers. This innovative solution communicates with users through various channels, ensuring that password issues are resolved quickly and efficiently.

Let’s dive deeper into the world of automated password fixing and discover how it can enhance your security.

Unlocking the Power of Automated Password Fixing

What is Automated Password Fixing?

Automated password fixing refers to the use of technology to manage and resolve password issues without human intervention. This innovative approach ensures that users can regain access to their accounts swiftly and securely.

By leveraging automation, you can avoid the frustration of lengthy recovery processes.

Why You Need It in Today’s Digital World

In an era where cyber threats are rampant, having a reliable password management system is crucial. Automated solutions not only save time but also enhance security by minimizing human error.

With the rise of data breaches, it’s essential to have a system that can quickly address password issues.

How OpsPilot Transforms Password Management

The Role of the AI Agent in Password Management

OpsPilot employs an AI Agent that intelligently handles password changes and recovery processes. This ensures a seamless experience for users, allowing them to focus on what truly matters.

With the AI Agent, you can trust that your password management is in capable hands.

Seamless Communication: No Human Intervention Required

With OpsPilot, users receive updates and instructions through various channels. This eliminates the need for manual support and reduces downtime.

Imagine receiving a notification on your phone that your password has been successfully changed—no waiting, no hassle.

Step-by-Step: Changing Your Password in 5 Minutes

How OpsPilot Guides You Through the Process

OpsPilot simplifies the password change process, guiding users through each step. This ensures a quick resolution, allowing you to get back to your day without unnecessary delays.

Have you ever found yourself stuck in a password reset loop? With OpsPilot, that’s a thing of the past.

Multi-Channel Communication: Stay Informed Every Step of the Way

Users are kept informed via email, SMS, or app notifications. This ensures they are aware of the status of their password change.

No more guessing games—just clear communication at every turn.

Benefits of Using Automated Password Fixing

Time-Saving: Get Back to Work Faster

Automated solutions drastically reduce the time spent on password recovery. This allows users to focus on their tasks rather than getting bogged down by password issues.

Wouldn’t you rather spend your time on productive activities?

Enhanced Security: Protect Your Accounts Effortlessly

By automating password management, OpsPilot minimizes the risk of security breaches caused by human error. This added layer of security is essential in today’s digital age.

You can rest easy knowing that your accounts are protected.

Real-World Applications of OpsPilot’s AI Agent

Case Studies: Success Stories from Users

Many organizations have successfully implemented OpsPilot. This has resulted in improved efficiency and user satisfaction.

How Businesses Are Leveraging Automated Password Fixing

Companies are adopting OpsPilot to streamline their IT support processes. This leads to faster resolutions and happier customers.

In a competitive market, efficiency is key

Getting Started with OpsPilot for Password Management

Easy Setup: A Quick Guide to Begin

Setting up OpsPilot is straightforward. It requires minimal effort from IT teams, making it accessible for everyone. Email sanju.burkule@appliedaiconsulting.com to get started.

Conclusion: Embrace the Future of Password Management

In today’s fast-paced digital landscape, automated password fixing is not just a luxury; it’s a necessity for security and efficiency. As cyber threats continue to evolve, so must our strategies for managing passwords.

Take Action: Start Using OpsPilot Today!

Don’t wait for a password crisis to occur. Implement OpsPilot and experience the benefits of automated password management.

By signing up for OpsPilot, you can start enjoying these advantages in just a few minutes. With OpsPilot, you’ll save time, enhance security, and streamline your password management process.

Why not take the first step towards effortless security today?

Cloud Security with Prowler: A Detailed Deep Dive

Mon, 25 Nov 2024 15:21:47 +0000

At AAIC, we prioritize cloud security and strive to provide insights into tools that simplify and strengthen cloud infrastructure management. One such tool explored during our internal sessions is Prowler, an open-source platform designed to enhance the security posture of cloud environments, especially AWS. Hope you can benefit from our analysis.

What is Prowler?

Prowler is an open-source security tool specifically built for cloud environments. It offers over 300 security assessments and supports more than 60 AWS services. These comprehensive checks include identifying misconfigurations, ensuring compliance with various frameworks, and providing actionable remediation guidance.

Prowler can scan for hundreds of potential security risks and vulnerabilities across AWS services and ensures that an organization’s AWS setup adheres to best security practices and key compliance standards such as:

CIS AWS Foundations Benchmark
PCI DSS (Payment Card Industry Data Security Standard)
HIPAA (Health Insurance Portability and Accountability Act)
GDPR (General Data Protection Regulation)

Key Features of Prowler

Extensive Security Checks: Prowler performs thorough scans to identify vulnerabilities and misconfigurations. Its robust framework evaluates cloud setups against benchmarks like CIS for AWS, GDPR, PCI DSS, and others.
Compliance Monitoring: With support for industry-standard frameworks, Prowler ensures adherence to compliance standards, critical for organizations catering to regulated industries.
Ease of Use: As an open-source tool, Prowler offers simplicity and flexibility in installation and operation. Users can deploy it on local machines, cloud services, or via Docker with minimal configurations.
Custom Reporting: Reports can be generated in multiple formats such as HTML, JSON, and CSV, providing tailored insights for stakeholders.
Integration Capability:

Prowler integrates seamlessly with services like AWS Security Hub, QuickSight, Slack, and SIEM platforms, ensuring streamlined security management.

Installation and Setup

Prowler’s installation process is straightforward:

It requires Python and compatible libraries.
For AWS users, specific IAM policies, such as SecurityAudit and ViewOnlyAccess, are essential to grant Prowler the necessary permissions.
Commands can be executed to perform scans, with or without specifying providers like AWS, Azure, or GCP.

Usage Insights

Prowler scans AWS environments for vulnerabilities, categorizing issues by severity (low, medium, high, critical).
The tool also highlights non-compliant areas, referencing AWS Well-Architected Framework and other best practices.
Its customization options allow exclusion of specific resources or regions, optimizing scan focus as needed.

Real-World Applications

Prowler’s ability to identify risks like unpatched software vulnerabilities in container images or inadequate IAM configurations makes it a vital tool for maintaining robust cloud security. Its actionable recommendations, linked to documentation, simplify remediation processes for technical teams.

Conclusion

Prowler exemplifies the efficiency of open-source tools in the ever-evolving landscape of cloud security. By providing detailed insights, robust compliance checks, and easy integration, it empowers organizations to secure their cloud infrastructure effectively. At AAIC, tools like Prowler are instrumental in helping clients ensure security, maintain compliance, and foster trust in their cloud solutions.

Schedule a demo today with us, to understand how Prowler can help you o enhance the security posture of cloud environments.

Book a meeting @ Calendly.com/aaic

Streamlining Automation for Ad Sales and Business Processes

Mon, 25 Nov 2024 15:02:22 +0000

In today’s fast-paced business landscape, efficiency and precision are essential, particularly in ad sales and operational workflows. Automated solutions are becoming indispensable, alleviating the burden of manual data entry and freeing teams to focus on more strategic initiatives rather than repetitive tasks.

A recently developed form extension feature has been introduced to transform ad sales data management through automation. This feature minimizes manual input by allowing users to upload an Excel file directly into a form-filling extension, enhancing both speed and accuracy in data handling.

Overview of the Form Extension Feature

This form extension feature streamlines ad sales data entry with a user-friendly design that supports direct Excel file uploads, drastically reducing manual input and enhancing data integrity. Below are its key functionalities:

File Upload Process: Users begin by uploading an Excel file containing ad sales data. The system then uses this file to auto-populate corresponding fields in the form, saving significant time and minimizing manual entry.
Automated Entry and Error Management: Not only does this feature automate data entry, but it also handles errors effectively. If there are any errors in the uploaded data, the tool skips those rows and processes valid entries, ensuring smooth operations without compromising accuracy.
Output Generation: Once the form is completed, the system generates an output file that includes a unique EPR (Entity Processing Record) number. This number is essential for tracking and invoicing, adding structure to data management, and simplifying subsequent business processes.

How the Form Extension Feature Works

The workflow of this form extension feature highlights its seamless operation:

File Upload: Users easily upload an Excel file with ad sales data. By eliminating the need for manual data entry, the system auto-populates the required fields from the uploaded file.
Error Handling: One of the standout functionalities of the tool is its error management. When data issues are detected, the tool skips erroneous rows and continues processing the valid ones. This reduces human error, enhances data quality, and improves overall data entry efficiency.
Output Generation: After completing the form, the system generates an output file with a unique EPR number, which aids in tracking and invoicing. This feature simplifies financial processes and ensures that essential data is easily accessible and well-organized.

Key Benefits of the Automation Feature

The implementation of this automated form extension feature provides organizations with several significant advantages:

Time Savings: Automated data entry drastically reduces manual input time, allowing teams to focus on more high-value tasks, such as strategy development and decision-making.
Improved Accuracy: By reducing human error through automated data processing, the system ensures that data is accurate and reliable. This leads to better decision-making based on trustworthy data.
Streamlined Invoicing: The generation of an output file with an EPR number simplifies invoicing by making all the necessary information readily available for financial operations. This reduces administrative overhead and improves cash flow management.

Scalability

As organizations grow, this feature can easily adapt to handle increasing volumes of data, offering a sustainable solution for ongoing data management needs. This scalability ensures that businesses can continue to benefit from automation as they expand.

Conclusion

The form extension feature marks a significant step forward in automating ad sales data management. Its potential to streamline workflows, boost data accuracy, and improve overall efficiency makes it a valuable tool for organizations across a wide range of industries.

As businesses continue to evolve in the digital age, adopting automation solutions like this will play a pivotal role in driving productivity and supporting organizational success. By embracing automation, organizations not only improve their data handling but also free up valuable resources to focus on strategic growth. This ultimately fosters greater operational success and positions businesses for long-term success in an increasingly data-driven world.

Book a Meeting @ Calendly.com/aaic

Transitioning to Kubernetes: Best Practices and Considerations

Mon, 25 Nov 2024 14:47:11 +0000

As the tech landscape evolves, adopting cloud-native architectures is no longer just an advantage but a necessity for organizations seeking agility and scalability. Kubernetes (K8s), a robust container orchestration platform, plays a pivotal role in this transformation. This blog dives deep into best practices and considerations for transitioning to Kubernetes, covering application conversion, database setup, pod management, troubleshooting, monitoring, security, daily operations, and legacy application migration.

1. Application Conversion to Kubernetes

The first step in transitioning to Kubernetes is transforming traditional applications into Kubernetes-compatible systems.

Goals

To ensure applications can operate seamlessly in a containerized environment while leveraging Kubernetes’ orchestration features.

Building Blocks

Application Foundation: Begin by identifying the dependencies, operating system requirements, libraries, and tools necessary for your application. Create a Docker image encapsulating these elements with clear instructions for application execution. Consumable
Resources: Applications often need credentials and configurations to function. Kubernetes manages these using Secrets for sensitive data and ConfigMaps for configuration files.
Outputs: Applications generate outputs, such as user-facing content, logs, and performance metrics. Kubernetes doesn’t natively manage logs or metrics. Integrate tools like ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and Prometheus with Grafana for performance monitoring.

2. Database Setup in Kubernetes

Managing databases in a Kubernetes environment requires careful planning due to the platform’s ephemeral nature.

Standard Practice: Avoid running databases in production on Kubernetes. Instead, use managed services like AWS RDS or Azure SQL for higher reliability and scalability.
For Testing and Non-Critical Use: – Databases like MySQL or PostgreSQL can be deployed as separate pods with persistent volumes.
While suitable for testing, this approach is generally unsuitable for production environments.

3. Pod and Network Management in Kubernetes

Efficient pod and network management are essential for application reliability and scalability.

Pod Structure: A single pod should ideally handle one functionality or service. Avoid running multiple containers in a pod, as failure in one container can disrupt the entire pod.
Scaling: Kubernetes scales at the pod level, allowing applications to handle increased workloads efficiently.
Network Policies: Kubernetes enforces network policies to regulate inter-pod communication. Pods communicate internally via service names, avoiding hardcoding IP addresses as they can change during pod recreation.

4. Troubleshooting Kubernetes Issues

Challenges are inevitable in Kubernetes environments. Here are common issues and solutions:

Frequent Issues:
Image Pull Errors: Causes include incorrect image tags, missing images, or credential mismatches.

– Pending Pods

Often due to insufficient resources on nodes or unavailable volumes.

Resolutions:
Image Pull Errors: Verify the image tags, ensure proper permissions, and update credentials. Pending Pods: Check cluster resource allocation and volume configurations.

5. Monitoring and Security in Kubernetes

Monitoring:
Tools like Prometheus and Grafana are industry standards for tracking Kubernetes cluster health and performance metrics.
These tools provide detailed insights into pod failures, resource utilization, and other critical metrics.
Security Practices:
For sensitive applications, implement tools like Microsoft Defender for vulnerability scanning.
Additional practices include running applications as non-root users and regular security audits.
Defender integration into CI/CD pipelines ensures continuous vulnerability checks during container builds

6. Daily Operations in Kubernetes

Efficient Kubernetes operations require a clear division of responsibilities:

Developer Responsibilities: Focus on coding application features and defining configurations like passwords and config files.
Administrator Responsibilities: Create Docker images, write deployment files, and manage Helm charts. Use Helm charts for deployment automation, which simplifies configuration management and reduces errors.

7. Migrating Legacy Applications to Kubernetes

Legacy applications present unique challenges, especially in logging and state management.

Challenges:
Traditional applications often store logs locally, which conflicts with Kubernetes’ ephemeral pod architecture.
In Kubernetes, pod-specific logs are lost when pods terminate.
Solutions:
Utilize external logging tools like Fluentd or the ELK Stack to centralize and retain logs.
Migration Steps:
Requirement Gathering: Understand application dependencies and workloads.

– Assessment

Evaluate application features and identify components needing adaptation for Kubernetes.

– Implementation

Gradually migrate components, validate features in the Kubernetes environment, and optimize performance.

Conclusion

Transitioning to Kubernetes is more than a technical shift; it’s a strategic move toward scalability, agility, and innovation. By addressing critical areas like application conversion, database setup, pod management, monitoring, and security, organizations can unlock Kubernetes’ full potential while ensuring a seamless migration for legacy applications.

Investing in best practices and robust tools during this transition will yield significant long-term benefits for organizations embracing Kubernetes as part of their cloud-native journey.

Book a Meeting @ Calendly.com/aaic

How OpsPilot Enhances Forensic Data Gathering: Automated Data Collection for Faster IT Issue Resolution

Wed, 13 Nov 2024 15:21:59 +0000

Streamlined Forensics: How OpsPilot Automates Data Collection for Rapid IT Issue Resolution

In fast-paced IT environments, every second counts when it comes to resolving issues that affect performance, security, and user experience. To ensure that IT support teams can respond effectively, having access to accurate, real-time forensic data is crucial. OpsPilot’s automated data collection scripts make this possible by intelligently capturing relevant details within seconds after an alert, arming support teams with the insights they need to resolve issues faster and more accurately. Let’s dive into how OpsPilot’s approach to data gathering and script automation is transforming IT support.

How OpsPilot’s Automated Data Collection Works

The goal of OpsPilot’s data collection feature is to automatically capture key information related to alerts as soon as they happen. Here’s how it works:

1. Auto-Generating Data Collection Scripts

When OpsPilot detects a new alert, it automatically analyzes both the alert details and host information to create a customized data collection script tailored to the issue. This script is designed to pull in only the relevant data required for diagnosing the specific problem. For example:

For a network connectivity issue, OpsPilot might generate a script to gather details on network latency, current bandwidth, and active sessions.
For a disk space alert, it may pull in disk usage statistics, recent file modifications, and potential storage bottlenecks.

By generating scripts that match the nature of the issue, OpsPilot minimizes unnecessary data collection, reducing both time and resource usage.

2. Read-Only, Safe Commands Only

Security is a core focus of OpsPilot’s data collection process. Every script is built using read-only, non-invasive commands, ensuring that data collection poses no risk to the system or the data integrity of the affected host. These commands are carefully selected to gather information without making any changes, preventing accidental disruptions or security breaches.

This “safe data collector” design allows IT teams to gather critical forensic information without compromising system stability.

Examples of read-only commands include:

df -h for gathering disk usage.
netstat -an for network connections.
ps aux for listing running processes.

This approach ensures that OpsPilot’s automated scripts can run confidently in sensitive environments, giving IT teams access to valuable insights without the risk.

3. Rapid Execution: Collecting Data Within Seconds

Timing is essential when dealing with critical alerts, as some data might be lost or become irrelevant as time passes. OpsPilot executes the generated data collection script within seconds of the alert, capturing forensic data while it’s still fresh and relevant. This rapid response allows OpsPilot to collect time-sensitive data, such as active connections, process states, and system resource usage, which are crucial for accurately diagnosing the issue.

4. Auto-Populating Ticket Information

Once the data collection script runs, OpsPilot doesn’t stop there. The information gathered is immediately updated into the associated IT support ticket, giving support engineers a comprehensive view of the issue without manual effort. This streamlined data entry minimizes delays and ensures that the support team has full context from the start, allowing them to focus directly on the resolution.

The ticket entry typically includes:

A summary of the issue detected.
Relevant forensic data collected by the script.
Host and system-specific information to aid in diagnosing the root cause.

5. Resolution Recommendations for Faster Support

In addition to data collection, OpsPilot goes one step further by recommending resolution scripts based on the collected data. After analyzing the data and recognizing common known issues, OpsPilot suggests a tailored resolution script to help the IT support team address the problem swiftly. These scripts are based on historical resolution data, troubleshooting best practices, and OpsPilot’s intelligent diagnostics.

For instance:

If a high CPU usage alert is triggered, OpsPilot might recommend a script to identify and terminate any rogue processes consuming excessive resources.
For a network-related issue, the tool might provide steps to reset the network interface or clear problematic sessions.

By offering resolution guidance directly within the ticket, OpsPilot enables support engineers to act immediately, drastically reducing time to resolution.

Benefits of OpsPilot’s Automated Data Collection and Resolution Recommendations

OpsPilot streamlines IT issue resolution by automating data collection, allowing faster, more accurate diagnoses. With immediate, precise forensic data and safe read-only commands, IT teams can work efficiently and confidently, backed by best-practice resolution scripts. Here are the benefits at a glance:

1. Enhanced Speed and Efficiency

By automating data collection and ticket updating, OpsPilot eliminates time-consuming manual steps, enabling IT support teams to begin diagnosis and resolution faster.

2. Improved Forensic Accuracy

Capturing forensic data within seconds of an alert ensures accuracy, helping IT teams understand the true nature of the issue without relying on potentially outdated information.

3. Reduced Risk with Read-Only Commands

Using read-only, non-intrusive commands keeps data collection safe and non-disruptive, preserving system integrity and preventing unintended side effects.

4. Empowered IT Support with Resolution Scripts

OpsPilot’s recommendation of resolution scripts allows IT support teams to implement fixes confidently and swiftly, leveraging best practices for each issue type.

Conclusion: Transforming IT Support with Intelligent Automation

OpsPilot’s automated data collection and resolution recommendation features are revolutionizing IT support. By generating customized, safe, and rapid data collection scripts and pairing them with actionable resolution steps, OpsPilot ensures that support teams have everything they need to resolve issues efficiently. This intelligent, proactive approach empowers IT teams to maintain high service levels, minimize downtime, and deliver a more responsive support experience.

Ready to see how OpsPilot can transform your IT support? Contact us for a demo and experience the future of automated, intelligent IT issue resolution.

Book a Meeting @ Calendly.com/aaic

To read about “How IT Copilots Empower Support Engineers with Coordinated Intelligence” please check this Blog

3000 IoT Devices Revolutionizing Hospitality Through Advanced Content Distribution and Cloud Infrastructure

Wed, 13 Nov 2024 01:08:48 +0000

Revolutionizing Hospitality: IoT-Powered Content Delivery and Cloud Solutions for Enhanced Guest Experiences

Introduction

The hospitality industry continues to embrace innovation through IoT and advanced technologies, transforming guest experiences and optimizing operations. Applied AI has helped several clients in past few years develop solutions that combine secure content distribution, efficient cloud services, and IoT-driven solutions tailored for hospitality. This platform enables hotels to seamlessly manage digital content across regions, delivering personalized guest experiences. These businesses typically look for IT solutions like smart room controls, contactless check-in, energy management, and personalized in-room entertainment, video image analytics, lift monitoring, guest pick up, drop off monitoring and more.

Key Features for Hotels:

Smart Room Controls: Manage lighting, temperature, and amenities with ease.
Contactless Check-In, Guest pickup monitoring to room check-in: Smooth guest entry and check-in process.
Energy Management for entire hotel and per room: Track and reduce energy usage for cost savings.

In-Room Entertainment: Connects with guests’ devices for personalized experiences.

To deliver these integrated solutions, the business collaborates with tech partners, providing essential technologies like:

IoT Sensors for monitoring and automation.
Entertainment Systems for personalized in-room experiences.
Door-Lock Systems, Temperature control for pools and Camera ensuring secure, contactless access.

Technology Stack and Architecture

Applied AI, an Advanced AWS Partner, utilizes AWS well architected services to ensure scalability, security, and operational efficiency. Below is the breakdown of its core components:

1. Content Distribution with CloudFront and Route 53

It specializes in distributing static content (e.g., media, hotel-specific information) using AWS CloudFront and Route 53 for DNS management. This combination ensures fast content delivery across regions, reducing latency and ensuring high availability.

2. Application Logic and Container Management with ECS and CloudWatch

Amazon ECS (Elastic Container Service) is used for hosting applications and managing containers via Fargate or EC2 instances. Containerization ensures easy updates, maintenance, and scalability.

AWS CloudWatch monitors ECS tasks, cluster performance, and system health, providing real-time insights, log aggregation, and automated alarms for critical thresholds.

3. ECS Autoscaling

Dynamically adjusts the number of ECS tasks based on traffic and resource utilization, optimizing performance during peak demand and minimizing costs during low-demand periods.

4. Authentication and Security with Keycloak/Cognito and AWS IAM

Keycloak/Cognito: An open-source identity and access management system that handles user authentication and authorization across applications.
AWS IAM adds an additional layer of security by managing permissions for AWS resources, ensuring secure access to critical cloud infrastructure.

5. Load Balancing with Public NLB and Private ALB

Public NLB (Network Load Balancer) handles external traffic, routing it to Private ALBs (Application Load Balancers) for internal traffic management within the VPC, ensuring secure and optimized routing of requests.

6. Cost Management and Monitoring with AWS Billing and Chronicle

AWS Billing: Provides usage insights, cost tracking, and budgeting tools, ensuring efficient resource utilization.
Chronicle: Manages job scheduling, including Cron jobs and HTTP requests across servers, ensuring that maintenance and data collection tasks run smoothly.

7. CI/CD Automation with Bitbucket

The platform uses Bitbucket for CI/CD, automating the deployment process and facilitating continuous integration and delivery for better management of the development pipeline.

Application Services

Several key services support the operational efficiency of the platform:

Admin (Smart Cloud): Centralizes administration tasks for easy management.
Monitoring (Smart Monitoring): Tracks the performance and health of the system.
Operation (Smart Access): Manages access control within the environment.
SmartApp and Smart-Remote: Designed for custom applications tailored to the hospitality industry.

Challenges Faced by Traditional IoT Platforms

The platform faced several technical obstacles that hindered its performance:

Outdated AWS configurations: The use of legacy AWS setups created compatibility and operational inefficiencies.
Deprecated images: Some images in the infrastructure were outdated, affecting stability.
Lack of modern security protocols: Missing security protocols, such as SSL, raised concerns around secure communication.

Transformations delivered by Applied AI

To overcome these issues, the platform implemented strategic updates:

Transitioned from Amazon EKS to ECS: This shift improved scalability and reliability.
Adopted Terraform: Terraform was introduced for more efficient infrastructure provisioning, simplifying deployment.
Enhanced security: Updated security measures were implemented to ensure better protection.

Conclusion

This technology stack highlights how cloud-native solutions, content distribution, and IoT integration can revolutionize hospitality operations. With cutting-edge capabilities for content management, containerization, and security, Applied Ai helps clients to build a AWS based platform ensurings a seamless experience for clients worldwide. As it expands into new markets, its adaptable, high-performance infrastructure will help elevate guest satisfaction and deliver unmatched digital experiences across the hospitality industry.

Book a Meeting to discuss your project: calendly.com/aaic

OpsPilots: Specialized IT Copilot Agents for Speeding Up Ticket Resolution

Fri, 08 Nov 2024 15:21:04 +0000

How IT Copilots Empower

Support Engineers with Coordinated Intelligence

Imagine this scenario: It’s a typical Monday morning, and your IT support team is overwhelmed with incoming tickets. Network outages, slow system performance, access issues—everything seems to be happening at once. The team is scrambling, trying to address each problem while keeping anxious users informed. It’s a situation all too familiar in IT departments worldwide.

Now, imagine having a set of specialized agents that can rapidly diagnose, troubleshoot, and resolve these issues—all in real-time, without human intervention. IT Copilot agents are designed exactly for this purpose. They assist support engineers in tackling common IT problems with speed and precision, allowing your team to focus on strategic improvements rather than firefighting day-to-day incidents.

In this blog, we introduce you to these specialized IT Copilot agents, each equipped to solve specific IT support challenges, from network disruptions and security threats to resource optimization and user communications. Let’s explore how they work individually and in collaboration to make IT support faster, smarter, and more efficient.

Each IT Copilot agent is equipped to handle specific types of issues, making it easier for support engineers to address tickets effectively. Here’s how these agents contribute to different areas of IT support.

The Coordination Agent: Orchestrating Efficient Ticket Routing

The Coordination Agent acts as the central brain of the IT Copilot system, analyzing incoming tickets and directing them to the most suitable agents based on the issue type and severity. This agent ensures that tickets reach the right specialized agent immediately, reducing delays caused by misrouted tickets and enabling faster response times.

In cases where the IT team has pre-built runbook scripts, the Coordination Agent further enhances efficiency by matching the appropriate script with the ticket. It then provides the support engineer with exact runbook steps to follow, based on proven workflows, saving time and ensuring consistency. If no runbook exists for a given issue, the Coordination Agent offers the option to proceed with the AI agent’s expertise instead. Only when the support engineer confirms, does the agent share tailored command suggestions to move forward, providing both flexibility and control to the IT team.

Example Workflow of the Coordination Agent

Ticket Assessment: When a new ticket arrives, the Coordination Agent scans the issue description, assesses the keywords, and analyzes historical ticket data to determine the category of the
Agent Assignment: Based on the ticket type, the Coordination Agent assigns the ticket

to the relevant agent(s)—such as the Network Resolution Agent for connectivity issues or the Storage Resolution Agent for data access problems.

Runbook Matching: If a relevant runbook script is available, it’s matched to the ticket,

and step-by-step instructions are provided. If not, the agent prompts the IT team to proceed with AI-assisted commands.

Help Staff: The Assigned Agent handholds the IT support staff to help address the

issue promptly.

By handling the routing, runbook script-matching, and initial assessment, the Coordination Agent allows support engineers to focus on solving issues faster, with the added advantage of having the right agents in place from the start.

Specialized IT Copilot Agents and Real-World Examples

1. Network Resolution Agent: Resolving Connectivity and Performance Issues

The Network Resolution Agent specializes in diagnosing and resolving network-related issues, such as connectivity disruptions, slow speeds, or configuration problems.

Examples:

VPN Connectivity Issues: For remote workers unable to connect to the VPN, the Network Resolution Agent checks gateway status, monitors for high latency, and recommends configuration updates if needed.
High Latency on Wi-Fi: When users report slow Wi-Fi speeds, the agent analyzes traffic patterns, identifies congestion points, and recommends adjustments to network settings or channels.
Intermittent Connectivity Drops: The agent detects patterns of network dropouts and may suggest adjusting firewall configurations or rebalancing network loads to stabilize connections.

Outcome: Network issues are diagnosed and resolved quickly, ensuring that users experience minimal disruption to their connectivity.

2. Security Resolution Agent: Addressing Access Control and Threat Mitigation

The Security Resolution Agent helps protect systems from unauthorized access and potential security threats. It monitors for security events, suggests mitigation actions, and ensures compliance with security policies.

Examples:

Unauthorized Login Attempts: If multiple failed login attempts are detected, the agent temporarily locks the account, alerts the support engineer, and suggests MFA (Multi-Factor Authentication) reset instructions.
Suspicious Network Activity: The agent detects unusual data transfer spikes on a user’s account, flags it as potentially malicious activity, and suggests temporarily blocking the user’s network access until further investigation.
Access Control for Sensitive Folders: When a user requests access to restricted data, the Security Resolution Agent verifies the request against access policies and either grants temporary access or suggests alternative solutions if access is denied.

Outcome: Security risks are mitigated swiftly, protecting sensitive data and maintaining user account integrity.

3. Compute Resolution Agent: Optimizing System Performance and Resource Allocation

The Compute Resolution Agent assists with issues related to computing resources, such as CPU and memory utilization, application slowdowns, and virtual machine (VM) management. Examples:

High CPU Usage: The agent detects a spike in CPU usage on a critical server and suggests closing background processes, redistributing workloads, or expanding CPU capacity to balance the load.
Memory Leak in Application: The agent identifies excessive memory usage in a web application due to a potential memory leak. It recommends commands to restart the application or temporarily shift load to another instance.
VM Provisioning and Scaling: For new application deployments, the Compute Resolution Agent suggests optimal VM configurations based on the anticipated workload, ensuring sufficient resources are allocated without unnecessary overhead.

Outcome: The Compute Resolution Agent prevents system slowdowns and optimizes resource usage, helping IT teams maintain high-performance levels across applications.

4. Storage Resolution Agent: Ensuring Data Availability and Integrity

The Storage Resolution Agent manages data access, storage capacity, and backup-related tickets, helping ensure data remains accessible and secure.

Examples:

Low Disk Space Warning: When disk space is running low on a shared storage system, the Storage Resolution Agent provides options for clearing unnecessary files, archiving old data, or expanding storage capacity.
Data Access Permissions: For tickets related to restricted folder access, the agent suggests verifying user permissions and granting appropriate access based on company policy.
File Recovery from Backup: If a user accidentally deletes a file, the Storage Resolution Agent identifies recent backup points and guides the support engineer through restoring the lost data from the backup.

Outcome: Data storage and access issues are resolved quickly, ensuring users can access necessary information without delays.

5. Human Interaction Agent: Enhancing User Communication and Ticket Updates

The Human Interaction Agent is designed to assist support engineers in communicating with end-users, updating ticket statuses, and ensuring users are informed about issue progress and resolution.

Examples:

Ticket Status Updates: When a ticket is escalated or resolved, the Human Interaction Agent updates to the user.
Guided Troubleshooting for End-Users: For common user issues like password resets, the agent provides user-friendly instructions and links to knowledge base articles if it was missed by the automated agents.
Feedback Collection: After a ticket is resolved, the agent sends a brief survey to the user to gather feedback on the support experience, helping the team identify areas for improvement. This can be automated if its same type of feedback.

Outcome: Communication with users is streamlined, leading to better support experiences and higher user satisfaction.

Special Use Cases and Multi-Agent Collaboration

The Coordination Agent can route complex tickets to multiple agents for a comprehensive approach. Here are some scenarios where multiple agents work together to accelerate ticket resolution.

1. Data Breach with Network Impact (Multi-Agent scenario)

Coordination Agent routes the ticket to both the *Security Resolution Agent *and Network Resolution Agent.
Security Resolution Agent initiates an immediate account lockdown and audits recent access logs.
Network Resolution Agent analyzes network traffic to prevent further unauthorized data access.

The agents collaborate to contain the breach, securing the network and sensitive data.

2. System Outage Affecting Multiple Services

Coordination Agent assigns the ticket to the Compute Resolution Agent, Network Resolution Agent, and Storage Resolution Agent.
Compute Resolution Agent checks for excessive load and suggests load balancing.
Network Resolution Agent identifies bandwidth bottlenecks and reroutes traffic to stable connections.
Storage Resolution Agent verifies data integrity and access to ensure no data loss.

Working together, the agents restore service uptime faster than isolated

Coordination Agent assigns the ticket to the *Storage Resolution Agent *and Security Resolution Agent.
Storage Resolution Agent checks and updates the user’s access permission.
Security Resolution Agent ensures that permissions are in line with compliance and prevents unauthorized

This dual approach minimizes security risks while ensuring appropriate access for the user.

Conclusion: Empowering Support Engineers with Specialized IT Copilot Agents

IT Copilots provide support engineers with a suite of specialized agents, each tailored to address a unique set of issues. By combining the coordination power of the Coordination Agent with focused assistance from Network, Security, Compute, Storage, and Human Interaction Agents, IT Copilot ensures that tickets are resolved quickly, accurately, and with minimal effort.

From simple password resets to complex network and security incidents, IT Copilot accelerates ticket resolution and elevates support quality, enabling IT teams to focus on strategic improvements and proactive issue prevention. With IT Copilot’s multi-agent intelligence, support engineers can transform their day-to-day operations and provide exceptional service every time.

Book a Meeting to discuss your project: calendly.com/aaic

To read about how OpsPilot automates ‘script generation’ for immediate forensic data collection based on alert and host information, read this blog

Document Extraction with AI: Automating Data Processing

Mon, 04 Nov 2024 16:19:17 +0000

With the exponential growth of unstructured data, businesses encounter significant challenges in extracting valuable insights from a variety of documents, including invoices, financial statements, contracts, and more. Document extraction using AI has emerged as a game-changing solution, enabling organizations to automate the process of extracting critical information from these documents with unmatched speed and accuracy. This blog will delve into the mechanics of AI-powered document extraction, examining tools and platforms such as AWS Textract, Azure AI Document Intelligence, Snowflake Document AI, Google Document AI, and custom Python-based solutions. Additionally, we will highlight a success story that demonstrates how technology can be mapped to deliver real value.

What is Document Extraction with AI?

Document extraction with AI refers to the use of artificial intelligence, particularly machine learning (ML), natural language processing (NLP) and optical character recognition (OCR) technologies, to automatically extract text, data, and key insights from unstructured or semi-structured documents. Unlike traditional OCR, which primarily focuses on recognizing text, AI-powered systems can understand document structures, handle complex formats, and extract tables, forms, and metadata with high precision.

Why is Document Extraction Crucial?

I’ve been talking to a CFO in a mortgage processing company and, lately, with an executive at an investment property management firm. In separate conversations, they both talked about the volume of back-office work processing documents using traditional OCR and/or a mix of manual and automated methods.

It’s a fact that businesses across industries like finance, healthcare, legal, and real estate deal with massive volumes of documents. And manually processing these documents is both time-consuming and error-prone.

Fig: Challenges with traditional and manual extraction tools

Hence automating document extraction can significantly improve operational efficiency by:

Increased Efficiency: Reducing time spent on manual tasks enhancing productivity.
Enhanced Accuracy: Minimizing errors in data extraction, ensuring reliable outcomes.
Cost Savings: Automating workflows leading to significant cost reductions.
Improved Compliance: Ensuring accurate data handling, and maintaining regulatory compliance.
Better Decision Making: Priding structured data for informed business decisions.

Fig: Benefits of AI powered Document Extraction

Document Extraction Use Cases

Now that we have a consensus that AI-powered document extraction can bring in significant speed and accuracy, the next steps for every organization are the following

Identify the use cases where this can help
Prioritize the use cases based on ROI or customers etc.
Zero down on tools or a combination of tools that can help solve the use case in the best possible way.

Let’s look at some of the use cases and related values across domains and industries that we have come across during our interaction with customers, prospects, and partners. This can help invigorate some thoughts when we are thinking about use cases and value for our areas of work or our customers.

Government: Automated form processing, document classification, fraud detection
BFSI / Mortgage: Extracting data from bank statements, invoices, and contracts for automated financial reporting, KYC compliance, loan processing, and claim processing.
Healthcare: Processing medical records, insurance claims, clinical trial documentation
Legal: Analyzing contracts and legal documents to automate compliance and contract lifecycle management.
Real Estate: Generating property reports by extracting information from lease agreements, mortgage documents, financial records, Investment analysis, etc.

Fig: Document extraction use cases across industries and domains

AI Tools for Document Extraction

By now, the use case is identified and prioritized. The next step is finding tools or a combination of tools that can help solve the use case.

And I know, despite it sounding cliche, in reality, every customer problem, though may sound similar, has its challenges and needs a specific solution. That’s where just knowing a tool doesn’t help. And that’s where companies like Applied AI Consulting or other AI native companies come and guide/help consult/implement.

At this point, let’s dive into some of the leading AI-powered document extraction tools and platforms.

1.AWS Textract

AWS Textract is a machine-learning service that extracts text, forms, and tables from scanned documents. It goes beyond simple OCR by recognizing complex document structures, such as key-value pairs and tables, making it ideal for extracting detailed data from financial statements, contracts, and more.

Textract can be integrated with other AWS services like Amazon Comprehend for natural language processing and Amazon SageMaker for custom model training. For businesses handling fintech or mortgage services, AWS Textract is a powerful tool that automates document workflows efficiently.

1.Azure AI Document Intelligence

Microsoft’s Azure AI Document Intelligence(formerly known as Form Recognizer) is another leading AI service that uses machine learning to extract text, tables, and key-value pairs from documents. Azure allows users to create custom models tailored to their document types, which is particularly useful for industry-specific formats.

Azure AI Document Intelligence integrates seamlessly with Azure Cognitive Search to further enhance document processing capabilities, making it an excellent solution for applications requiring both document extraction and search capabilities.

1.Google Document AI

Google Document AI is a powerful platform that utilizes advanced machine learning to extract information from various document types, such as invoices and receipts. It offers pre-trained models for quick deployment and allows for custom model development for specific needs. Seamlessly integrated with Google Cloud services, it automates document processing, making it ideal for industries like finance and healthcare. Key features include intelligent character recognition, form parsing, and table extraction, enabling organizations to enhance efficiency and unlock insights from unstructured data.

Custom Python-Based Solutions

For organizations requiring full flexibility, custom Python-based solutions offer a powerful alternative. Python libraries such as Tesseract OCR for text recognition, along with frameworks like SpaCy for natural language processing, allow developers to build tailored document extraction solutions.

Custom Python scripts can be combined with regular expressions (regex) to handle specific document structures

Now that we have more learned about different AI-powered document extraction tools, let us look at a quick comparison to help select the right tool as per your use case and engineering needs.

If you notice, we have added a custom Python-based solution too since no tool is complete and you would need some amount of coding to get the best for your use case also sometimes when the need is for an on-prem solution, that’s where it helps.

All tools and platforms are good and evolving day by day. We, at Applied AI Consulting, work with one or more of these for our different customers or POCs and hence more current with changes and values based on the use cases.

In this blog, we are comparing these tools across the aspects like

Model Training & Customization
Document Support & Processing
Language Support & OCR Accuracy
Implementation & Integration
Cost & Scalability

We subjected the same set of documents across these toolsets to compare them across these different aspects.

Fig: Comparative Analysis of AI-Powered Document Extraction Tools

Quick Insights: Automating Financial Data Extraction

We have worked with multiple customers helping with automated data extraction using different hyperscaler-based tools and open-source custom Python libraries.

Let us look at the use case of a property management company overseeing multiple strip malls and investment properties.

They faced challenges with their slow, manual financial reporting process. Handling hundreds of tenant payments, vendor expenses, and financial documents resulted in delayed decision-making and increased operational costs. Applied AI Consulting was brought in to automate the extraction, categorization, and reconciliation of data from various documents.

Key Challenges

Diverse document formats
Time-consuming manual processes
High risk of human error
Lack of real-time reporting

Solution Implemented

Used AWS Textract and custom regex for efficient data extraction
Integrated DynamoDB for report tracking and S3 for secure document storage
Deployed ECS containers to enhance report generation speed

Results

95% reduction in financial report generation time
Over 90% reduction in manual errors
50% of team time freed up for strategic tasks

This case study demonstrates how automation can significantly improve financial data processing, leading to faster decision-making and increased operational efficiency.

Fig: Agentic framework for financial data extraction

You can read more about this case study here – https://appliedaiconsulting.com/automating-financial-data-extraction/

Wrapping Up: The Future of Document Automation

AI-powered document extraction is revolutionizing the way businesses handle unstructured data. By leveraging platforms like AWS Textract, Azure AI Document Intelligence, Google Document AI, Snowflake Document AI, and custom Python solutions, companies can automate the extraction process, reduce manual work, and enhance the accuracy of their data.

At Applied AI Consulting, we are at the forefront of AI innovation, helping businesses harness the power of document extraction to streamline operations and improve decision-making. Ready to transform your document processing workflows?

Contact us today to learn more. Disclaimer: The author has used generative AI assistance to generate images and Applied AI accelerator marxeed to structure the blog.*

Accelerating CI/CD Efficiency From Jenkins to GitHub Actions for Seamless Automation

Wed, 23 Oct 2024 18:10:34 +0000

Accelerating CI/CD Efficiency: From Jenkins to GitHub Actions for Seamless Automation

Transforming Development Workflows with Enhanced Security and Streamlined Processes

Company Overview

Client/Customer Overview:

The client is an organization that has been using Jenkins for years to manage their CI/CD processes. With a growing focus on optimizing operations, they were looking for more efficient, secure, and cost-effective CI/CD tools to address issues related to their existing setup. The business needed a solution that would streamline development workflows, improve security, and reduce operational overhead.

Feature-Rich Environment: The new CI/CD tool offers a wide array of features, including seamless integration with popular development and deployment platforms, allowing for smoother workflows.

Enhanced Security Measures: The product incorporates advanced security protocols and compliance features, ensuring that the CI/CD process adheres to industry standards and best practices.

Scalability and Performance: It is designed to scale effortlessly with the organization’s growth, ensuring high performance even during peak workloads.

Automation Capabilities: The tool automates various aspects of the CI/CD pipeline, reducing manual effort and minimizing the risk of human error.

User-Friendly Interface: The product provides an intuitive and user-friendly interface, enabling teams to quickly adopt the tool and reduce the learning curve associated with new technology.

Challenges

Security vulnerabilities

The old Jenkins server was difficult to update, posing significant security risks.

Complex setup

Jenkins required complex configurations, making even small changes time-consuming.

High costs

The continuous operation of Jenkins servers (24/7) resulted in high operational costs.

Performance inefficiencies

The Jenkins CI/CD pipelines were slower compared to newer alternatives like GitHub Actions

Let’s discuss your use cases

Operations Conducted

Alternative tool evaluation

The team explored other CI/CD options and chose GitHub Actions for its simplicity and integration with GitHub Enterprise.

Migration

They started with low-risk CI/CD pipelines to GitHub Actions and gradually moved their backend, frontend, and mobile services.

QA automation

The team also migrated the QA automation and mobile CI/CD for Android and iOS to GitHub Actions.

Strategic Approach

Seamless migration to GitHub Actions

The client adopted GitHub Actions due to its ease of use, eliminating the need for manual setup and reducing dependency on external infrastructure.

Improved security

GitHub-managed runners ensured up-to-date systems, significantly reducing security risks by 40%.

Optimized resource usage

The GitHub Enterprise plan was fully leveraged, maximizing resource utilization by 70% through built-in features like caching.

Enhanced developer productivity

By reducing complex setup and configurations, the team focused more on development, minimizing time spent on CI/CD management by 30%.

Positive Influence

Reduced CI/CD runtime

Significantly faster pipelines helped in optimizing release cycles and increasing overall efficiency by 30%.

Enhanced security

Compliance with SOC 2 audit standards was achieved, improving the business’s security posture by 20%.

Cost savings

Reduced human effort on complex setups and optimized usage of GitHub resources led to overall cost reductions of 90% on CICD servers.

Long-term Scalability

Custom dashboards: The team is working on building a consolidated dashboard for CI/CD monitoring and reporting, improving visibility.
Refined permission management: Plans include implementing least-privilege access controls for GitHub Actions to improve security.
Credential management: The client will enhance secret and credential management within GitHub Actions to ensure safer operations.
Broader adoption: The solution can be scaled to support other projects, enhancing efficiency and security across different teams and departments.

Key Takeaways

Migrating from Jenkins to GitHub Actions has resulted in significant improvements in operational efficiency, security, and cost-effectiveness. The streamlined CI/CD process has empowered the client’s development teams to focus more on product innovation while ensuring that their infrastructure remains secure and scalable for future needs.

Customer Testimonials

Switching to GitHub Actions has been a game-changer for our CI/CD workflows. The migration process was smooth, and the reduction in operational overhead and security risks has significantly improved our development efficiency. We can now focus on delivering value without worrying about complex infrastructure management.
Head of QA Engineer

Transforming QA Efficiency: Achieving 80% Regression Time Reduction through Automation

Wed, 16 Oct 2024 12:19:33 +0000

Transforming QA Efficiency: Achieving 80% Regression Time Reduction through Automation

Streamlining Quality Assurance with Automated Testing for Faster Results

Company Overview

A leading sales and marketing tech organization partnered with Applied AI Consulting to enhance the quality assurance of their campaign analytics application. The analytics application provides users with comprehensive insights into campaign performance. Its core functionality revolves around generating reports based on selected instructions, enabling users to gain valuable data-driven insights to optimize their campaigns. The application is organized into several key modules, each playing a critical role in delivering a complete analytics experience.

Dashboard: Displays campaign data visually through charts and graphs, offering an overview of campaign performance. It also provides key information on campaign assets and leads.

Campaigns: Lists all current and past campaigns, showing important metrics like assets, leads, and total campaign value. This module helps users track and analyze the performance of individual campaigns.

Report Module: Allows users to generate reports based on campaign data by selecting specific FAQs. These reports offer detailed insights into campaign outcomes, customized to the user’s needs.

Templates: Provides pre-built templates that users can apply to generate reports across different campaigns, saving time and ensuring consistency in report creation.

FAQs (Frequently Asked Questions): For each FAQ, report generation instructions are crafted using ChatGPT prompts. Based on these predefined instructions, the system generates corresponding reports. When a user selects an option from the FAQ list, the system utilizes the instruction linked to that selection to generate the report automatically.

User Management: This module enables administrators to manage user accounts, assign roles, and define access levels, ensuring secure and efficient use of the platform.

This modular structure offers flexibility and scalability, allowing users to quickly generate customized reports and gain actionable insights from their campaign data.

Challenges

Dependency on Manual Testing

Manual testing was the primary method for testing new features, causing delays and limiting scalability as it consumed significant time and resources for each release.

Incomplete Automation Framework

Although an external expert had initiated the automation framework (Cucumber Selenium with Java), it required further completion and alignment with the development team to be fully functional.

Framework Instability & Resource Strain

Frequent failures in the automation framework during daily pipeline executions led to unreliability, forcing the QA team to rely on manual testing for around 40-50% of the process, especially as new features were continuously added.

Coordination Challenges

The team struggled to synchronize manual and automation testing efforts with developers, particularly due to the division of responsibilities between the two testing methods.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Operations Conducted

Completion and Stabilization of Automation Framework

The team completed and stabilized the Cucumber Selenium framework (Java-based) that had been left incomplete by an external expert. By addressing failures, they ensured reliable automation execution, significantly reducing manual intervention.

Introduction of Structured Test Packs

Three key test packs were automated:

Sanity Test Pack (25 test cases across all environments)
Regression Test Pack (100+ daily tests via Jenkins)
Post-Deployment Sanity Checks to monitor application health after each deployment

Enhanced Automation Across Environments and Reduced Manual Effort

Automation was expanded across Develop, Staging, and Production environments, ensuring thorough sanity and regression testing. This reduced manual testing efforts by 40-50%, allowing the team to focus on testing new features and improving overall efficiency.

Strategic Approach

Stabilization of Automation Framework for Reliable Deployment

The QA team successfully stabilized the Cucumber Selenium framework, integrating it into the deployment process. This resulted in reliable daily test executions, with failure rates dropping to 5-10%, and ensured no major bugs reached production.

Implementation of Structured Test Packs for Efficiency

Introducing structured test packs—Sanity, Regression, and Post-Deployment Sanity Checks—optimized the testing process, saving 70% of the time previously spent on manual testing. Automation provided accurate and timely reports, enhancing QA efficiency.

Enhanced Time and Cost Efficiency

Automation saved approximately 70% of the time previously required for manual testing and reduced QA costs by 50-60%, especially during the development phase. This efficiency accelerated production releases and reduced overall expenses

Positive Influence

Enhanced Time and Cost Efficiency

Automation achieved a remarkable 70% reduction in time previously spent on manual testing and cut QA costs by 50-60%, particularly during the development phase. This boost in efficiency facilitated quicker production releases and lowered overall expenses.

Improved Accuracy and Reliability

By minimizing human errors, automation enhanced testing accuracy by 70%, leading to more consistent outcomes and higher-quality production deployments. This improvement ensured that the software met stringent quality standards with every release.

Continuous Stability Assurance

Daily automated test runs and post-deployment sanity checks ensured ongoing application stability. This approach enabled regular updates without jeopardizing core functionalities, while Jenkins pipelines and Slack notifications provided swift issue resolution.

Long-term Scalability

Continuous Expansion of Automation Coverage

As new features and functionalities are introduced, the automation framework can be continuously expanded. By regularly integrating new test cases into the existing test packs (e.g., sanity and regression), the framework will be able to handle future product updates efficiently, further reducing manual testing efforts.

Cross-Environment Automation

The current setup, which supports automation across Develop, Staging, and Production environments, is scalable to additional environments if needed. Automation can also be extended to handle complex integration tests, performance tests, and end-to-end scenarios.

Advanced Reporting and Analytics

Integrating advanced analytics into the testing pipeline will provide deeper insights into trends, failure patterns, and performance, helping to predict issues earlier in the development cycle. This can lead to smarter decisions for future releases and improved test case management.

CI/CD Pipeline Optimization

As the CI/CD process is further streamlined with tools like Jenkins, there is room to optimize the pipeline with more frequent releases, quicker feedback loops, and integration of newer DevOps tools to enhance automation efficiency.

Key Takeaways

The shift towards automation in the project has greatly improved the quality assurance process, reducing both time and cost while delivering more reliable, accurate test results. Automation helped resolve 70-80% of the issues and stabilized the testing framework, contributing to faster production releases with minimal failures. The introduction of structured test packs for sanity, regression, and post-deployment checks ensured consistent validation across all environments. With the successful integration of automation, the project is well-positioned to scale its testing process as new features are introduced, ultimately providing ongoing business value through time savings, cost reductions, and improved accuracy. Continuous improvements in the automation framework and CI/CD pipeline will further enhance its capability to support rapid and reliable software deployments in the future.

Customer Testimonials

AAIC's continuous and instrumental support in QA has been exceptional. Their ability to take charge of manual testing and swiftly transform it into automated processes has greatly improved the overall efficiency. The implementation of regression and sanity test automation across all environments has ensured seamless and reliable QA validation post-deployment. Their thorough and diligent approach has been recognized as a key factor in enhancing the testing process, contributing to faster, more robust deployments.
Head of QA Engineer

Automating Financial Data Extraction: Transforming Reporting with AI

Tue, 08 Oct 2024 15:22:32 +0000

Automating Financial Data Extraction: Transforming Reporting with AI

Streamlining Financial Processes for Enhanced Accuracy and Efficiency

Company Overview

A property management company managing multiple strip malls and investment properties was burdened with a slow, manual financial reporting process. The company needed a solution to streamline their handling of hundreds of tenant payments, vendor expenses, and financial documents, which resulted in delayed financial decision-making and increased operational costs. Applied AI Consulting was engaged to automate the extraction, categorization, and reconciliation of data from a variety of documents, drastically reducing manual effort and increasing reporting accuracy.

Issue

Diverse Document Formats

The client managed multiple financial documents like PDFs, check registers, and tenant reports, all with varying formats, making consistent data extraction a challenge.

Time-Consuming Manual Process

Manually processing hundreds of documents for report generation took weeks, delaying financial decision-making and slowing down operations.

High Risk of Human Error

The manual effort involved in reconciling data across numerous documents led to frequent human errors, compromising the accuracy of financial reports.

Lack of Real-Time Reporting

Due to the slow, manual process, the client was unable to generate timely financial reports, impacting their ability to make swift, data-driven business decisions

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

Automated Data Extraction

Used AWS Textract and custom regex to extract financial data from various documents efficiently.

Data Traceability & Security

Integrated DynamoDB for report tracking and S3 for secure document storage, ensuring data integrity.

Optimized Workflow

Deployed ECS containers to enhance the speed and efficiency of financial report generation.

Solution

Automated Data Extraction

Applied AI Consulting implemented AWS Textract for extracting data from financial documents, using custom regex for identifying key details.

Data Processing and Traceability

The system utilized DynamoDB to track report statuses, ensuring complete data traceability throughout the process.

Secure Storage and Processing

Input documents and final reports were securely stored in S3, while ECS containers managed the processing pipeline for seamless automation.

Automated Financial Report Generation Workflow Using AWS Services

Impact

Faster Financial Reporting

Cut financial report generation time by 95%, allowing property managers to make quicker, data-driven decisions.

Improved Data Accuracy

Reduced manual errors by over 90%, ensuring more accurate financial reports and reconciliation.

Resource Optimization

Freed up 50% of the team’s time, enabling employees to focus on strategic tasks rather than repetitive document processing.

Customer Testimonials

Applied AI Consulting’s automation reduced our reporting time from weeks to minutes while delivering unparalleled accuracy. The ability to extract data from our complex documents allowed us to make better business decisions faster.
Principal Executive, Property Management Client

Revolutionizing QA Testing: Reducing Regression Time by 80% Through Advanced Automation

Tue, 08 Oct 2024 11:16:51 +0000

Revolutionizing QA Testing: Reducing Regression Time by 80% Through Advanced Automation

Transforming Software Quality Assurance for Faster and More Reliable Releases

Company Overview

A leading software development firm partnered with Applied AI Consulting to enhance the quality assurance of their web application. The project involved comprehensive performance testing to evaluate the solution’s ability to handle large data sets and assess the API’s performance under high user loads.

The application integrates with platforms like LinkedIn to provide users with detailed, up-to-date information about companies and individuals. Key features of the application include:

Data Retrieval: Fetches comprehensive data on employees, branches, and companies from LinkedIn.

Search Functionality: Enables users to search for companies or employees, delivering relevant data quickly.

Credit System: Users spend credits to unlock specific information such as contact details.

Team Management: Managers can oversee credits, control team access, and share data among team members.

Bulk Updates: The application performs regular data updates, typically monthly, to reflect changes such as employee shifts or new company branches.

Admin Portal: An internal tool used by the product owners to manage credits and access control, ensuring smooth operations.

Application’s robust features and Applied AI Consulting’s performance testing helped ensure the platform could efficiently scale, delivering accurate, timely data under heavy user demands.

Issue

Manual to Automated Testing: Transitioning from manual to automated testing reduced regression time from 8 hours to 1.5 hours, saving 6.5 hours per run, improving speed and reliability in the QA process.

Testing Environment Issues: Local testing environments were unreliable and lacked visibility, prompting a shift to centralized, accessible systems for better test management.

Random Test Failures: Random failures occurred due to shared resources during parallel testing, resolved by using separate test environments and accounts.

Performance Testing: Automated stress tests (25-50 users over 12-24 hours) ensured application stability under load, integrated into the release pipeline for continuous performance assurance.

Let’s discuss your use cases

Actions Taken

Automated Testing with Selenium and Cucumber

The team automated over 320 test cases using a Selenium Java Cucumber framework, reducing testing time and eliminating human error. The suite runs independently, improving efficiency.

Jenkins & GitHub Integration

The automation suite was integrated with Jenkins for continuous testing and GitHub for version control, allowing tests to be scheduled and results to be shared, boosting collaboration.

Performance Testing & Failure Resolution

JMeter simulated 50 concurrent users to ensure stability under load, while private accounts and dedicated environments resolved random test failures caused by concurrent testing conflicts.

Solution

Reduced Regression Test Time

Shortened Sprints: Faster testing enables quicker completion of sprints, allowing for rapid feature iterations.

Quicker Go-to-Market Timelines: Accelerated testing facilitates faster transitions from development to production, helping the organization launch new features ahead of competitors.

Improved Testing Reliability

Enhancements led to fewer random test failures, creating a more reliable and stable testing environment.

Business Value of the Outcome

Cost Reduction: The reduced testing time significantly lowered AWS usage costs, achieving around 80% savings in overall expenses.

Faster Delivery Cycles: Streamlined processes enabled quicker transitions from staging to production, enhancing responsiveness to market demand.

Value Stream

Automated Testing Improvement: The effectiveness of the automated testing framework was enhanced, yielding more accurate and timely results.

Faster Decision-Making: The improved testing process provided quicker insights into application readiness, allowing clients to make informed decisions promptly and facilitating faster go-to-market strategies.

This diagram illustrates the integration of CI/CD pipelines for code development and testing, alongside an automated test framework using BDD practices, Jenkins for execution, and TestRail for tracking.

Impact

Increased Efficiency

Automation and parallel processing significantly reduced manual overhead, leading to a 50% increase in testing throughput. Regression testing time dropped from over 8 hours to just 1 hour and 30 minutes, yielding substantial cost savings for the client.

Enhanced Application Stability

Continuous performance testing has built confidence in the application's reliability during real-world usage. This proactive approach has led to a 30% reduction in post-deployment incidents, minimizing critical failures and improving overall application performance for users.

Improved Client Satisfaction

Faster testing cycles deliver accurate results, enhancing client trust and satisfaction. With quicker insights, clients can make informed decisions more rapidly, improving their overall experience and fostering stronger relationships with the testing team.

Customer Testimonials

Collaborating with Applied AI Consulting has revolutionized our QA processes for our application. By implementing an automated testing framework with Selenium, Java, and Cucumber, we reduced our regression testing time from over eight hours to just 1 hour and 30 minutes--an 80% reduction. This efficiency has allowed us to shorten sprints and launch new features faster than ever, giving us a competitive edge. The integration with Jenkins for continuous testing has also enhanced reliability, decreasing random test failures significantly. Moreover, we've achieved approximately 80% savings in AWS costs and a 30% drop in post-deployment incidents, greatly improving our client satisfaction. Thanks to Applied AI Consulting, we're now better positioned for sustainable growth and success.
Head of Engineer

Enhancing System Scalability and User Experience: A Performance Testing Case Study

Thu, 26 Sep 2024 18:40:11 +0000

Enhancing System Scalability and User Experience: A Performance Testing Case Study

Ensuring Scalability and Reliability Through High-Volume Data Processing and Stress Testing

Company Overview

A client using Applied AI Consulting’s services required performance testing for their application. The aim was to assess the application’s performance by uploading large data sets and determining how long the system takes to validate a batch file. Additionally, it was crucial to test how the API handles high user load and stress scenarios to ensure reliability, scalability, and responsiveness during real-world operations.

Issue

Ensuring the application operates smoothly under heavy load, preventing costly outages and disruptions.

Quick and efficient data validation to keep end users satisfied and avoid delays in critical business operations.

Ensuring the system can manage larger data sets to support business growth and seasonal traffic spikes.

Maintaining high performance levels to accommodate expanding user bases without compromising system efficiency.

Let’s discuss your use cases

Similar Use Cases

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Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

Batch File Uploading and Validation

Tested the system’s ability to handle substantial data uploads and efficiently validate large datasets without performance degradation.

Checking Batch Status & Exporting Completed Files

This tested the system’s ability to report on and export data without affecting ongoing operations.

Credit Validation

We also tested how credit checks are performed under different user loads.

Solution

Scalability Assurance

With data showing the system could handle up to 10M records, the client gained confidence that they could scale their operations without worrying about performance issues

Operational Efficiency

Identifying and addressing bottlenecks early ensured the system could handle high traffic without slowing down, leading to fewer disruptions and better user experiences.

Improved Decision-Making

The insights from the tests allowed the client to make informed decisions about where to optimize infrastructure for better performance.

Impact

Scalability

The application’s ability to manage up to 10 million records and scale with growing user demands gave the client confidence in their platform’s future growth potential.

Optimized User Experience

Performance improvements led to faster system responses, resulting in higher user satisfaction and better customer retention.

Informed Decision-Making

Detailed reports enabled the client to prioritize investments in infrastructure and optimizations, ensuring resources were allocated effectively for maximum return on investment.

Customer Testimonials

Working with Applied AI Consulting has significantly improved the performance of our application. Their thorough testing helped us identify critical bottlenecks, allowing us to scale effectively while maintaining system stability and delivering a seamless user experience under heavy load.
Head of Engineer

Quality, Application Performance: How to Enhanced Scalability and Reliability Through Comprehensive Load Testing

Thu, 26 Sep 2024 17:00:09 +0000

Quality, Application Performance: How to Enhanced Scalability and Reliability Through Comprehensive Load Testing

Achieving Seamless User Experience and System Stability Through Targeted Load Testing

Company Overview

A client of Applied AI Consulting, sought performance testing for their reviews and ratings application, which is utilized by major e-commerce platforms like Flipkart. The goal was to evaluate how the API performs under high user load and stress conditions to ensure the platform’s reliability, scalability, and responsiveness during peak usage in real-world scenarios.

Issue

Ensured the application could handle increased user loads, supporting future growth and preventing service interruptions. (Tools: Apache JMeter, aiTest)

Prepared the platform for handling peak traffic and business expansion.
Prevented system failures and downtime during high-demand periods.

Identified and fixed performance issues in critical functionalities to maintain consistent service delivery. (Tools: Apache JMeter, aiTest)

Ensured uninterrupted service availability for users.
Maintained low error rates and fast response times.

Measured the impact of user loads on application performance to guide system improvements. (Tools: Apache JMeter, aiTest)

Improved system efficiency and user satisfaction.
Enhanced responsiveness for a smoother user experience.

Utilized Apache JMeter and aiTest to simulate user interactions and evaluate performance across various API endpoints, ensuring comprehensive functionality coverage.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

Implemented Load Testing Tools

Applied Apache JMeter and aiTest to simulate user interactions and assess performance across various API endpoints, ensuring a thorough evaluation of critical functionalities.

Conducted Gradual Load Testing

Tested the platform with user loads ranging from 100 to 5000 to assess system performance and identify potential areas for optimization.

Analyzed Performance Data

Reviewed data to detect performance degradation and bottlenecks, providing actionable insights for system improvements.

Solution

Response Time Insights

 Identified significant performance slowdowns with increased user loads, highlighting areas needing improvement to ensure faster user interactions. (Tools: Apache JMeter)

Revealed performance issues that needed addressing to maintain efficiency. Guided optimizations to improve response times and service quality.

Failure Rate Identification

 Found high failure rates in critical processes under heavy loads, pointing out where reliability enhancements were necessary. (Tools: aiTest)

Highlighted reliability issues affecting user experience. Enabled focused improvements to prevent system failures.

Bottleneck Detection

Pinpointed key bottlenecks in processes, allowing for targeted enhancements to improve system performance and user satisfaction. (Tools: Apache JMeter, aiTest) Identified specific performance issues causing delays. Enabled precise optimizations to enhance overall system reliability.

Customer Testimonials

Applied AI Consulting's performance load testing was crucial in helping us identify and resolve bottlenecks in our application. They tested our system under heavy user loads, pinpointing areas for improvement, which enhanced our platform's scalability and reliability. Their insights allowed us to optimize performance, ensuring a better user experience even during peak demand. We highly recommend their expertise
Head of Engineer

Quality – Performance and Reliability: How to Leveraged Automation Testing to Handle 30,000 Concurrent Users

Thu, 26 Sep 2024 16:29:33 +0000

Quality - Performance and Reliability: How to Leveraged Automation Testing to Handle 30,000 Concurrent Users

Enhancing Scalability and User Experience Through Advanced Automation Solutions

Company Overview

An education technology platform that helps small and medium-sized businesses launch and manage online training products. It provides a comprehensive suite of tools for managing training businesses, including services like course creation, event management, and payment integration. The system architecture is based on microservices and Kubernetes.

Issue

Validate the system’s capability to manage up to 30,000 concurrent users without performance issues, essential for supporting large-scale online training events.

Test critical workflows, including event and course checkouts, to ensure reliability under high user demand.

Automate complex workflows across microservices to validate resilience and maintain system integrity during peak usage periods.

Ensure seamless user interactions and maintain high service quality through rigorous testing and performance optimization.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

Implemented Automation Tools

Utilized advanced automation tools to streamline testing processes, ensuring comprehensive coverage and efficiency.

Progressive Load Testing

Gradually tested the platform’s capacity from 1,000 to 30,000 users, allowing for precise performance tuning a

Optimized Performance and Reporting

Addressed bottlenecks, enhancing system speed and reliability, while delivering detailed reports to boost stakeholder confidence.

Solution

Scalable Performance

Successfully supported up to 30,000 concurrent users, demonstrating the platform’s capability to handle high demand without compromising performance.

Improved System Reliability

Resolved key issues, leading to a robust system that operates reliably under peak conditions.

Enhanced User Experience

Achieved a 2-second page load time, significantly improving user satisfaction and engagement.

Informed Decision-Making

Provided stakeholders with comprehensive performance reports, facilitating better strategic decisions and reinforcing trust in the platform.

Impact

Enhanced Scalability

Efficiently managed thousands of concurrent users, ensuring the platform can handle peak demands.
Increased capacity for high user volumes.
Prepared for future growth and expansion.

Cost Savings

Reduced the need for manual testing, saving time and resources.
Lowered testing costs.
Decreased labor and resource expenditures.

Improved Reliability

Boosted platform stability by resolving performance issues, leading to consistent service quality.
Increased system dependability.
Minimized downtime and disruptions.

Customer Testimonials

Our client's ed-tech platform, faced challenges with reliability and scalability. Before aiTest, we manually tested our complex microservices architecture, but it didn’t provide the confidence we needed. Working with aiTest, we scaled our load testing from 1,000 to 30,000 users, identified key issues, and resolved them efficiently.

Their all-in-one solution helped us meet our SLA of two seconds per page load, and the detailed reports boosted client confidence. aiTest has been invaluable in ensuring our platform can scale and perform reliably, and I highly recommend their services.
Makarand Bhatamrekar

AI Driven Secure Search for Federal Agencies

Mon, 23 Sep 2024 10:17:24 +0000

AI Driven Secure Search for Federal Agencies

Enhancing Data Security and Search Efficiency for Federal Agencies with AI Solutions

Company Overview

U.S. federal agency – Wanted to see impact of AI Driven search within the agency. Search had to to be secure and scalable.

Issue

Needed to manage petabytes of documents with high-speed search, crucial for handling extensive data efficiently.

Role-Based Access Control (RBAC) is required to protect sensitive information and meet stringent federal security standards.

Implemented advanced AI for contextual and generative search functionalities to enhance user experience and efficiency.

Ensured the platform adhered to federal data handling and security regulations, safeguarding sensitive information.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

Scalable Platform and AI Management

Designed a secure, scalable solution on Azure, integrating Mistral for efficient AI model and data management to improve system performance.

Enhanced Search

Developed Retrieval-Augmented Generation (RAG) to improve search accuracy and speed, optimizing user experience.

Document Handling

Integrated proprietary technology for efficient document crawling and embedding, streamlining data management.

Solution

High-Speed Handling

Enabled rapid processing of large volumes of documents, crucial for federal operations.

Enhanced Security

Ensured data protection and compliance with federal standards through RBAC

User Experience

Improved search accuracy and speed, boosting productivity and efficiency.

Consistent Quality: Ensured high-quality content with fewer revisions, improving efficiency and client satisfaction.

Impact

Faster Onboarding

Cut onboarding time by 50%, making it quicker for employees to access necessary documents

Enhanced RFP Response

Reduced the time needed to respond to requests for proposals (RFPs) by 40%, improving the speed and quality of bid submissions.

Improved Maintenance Efficiency

Lowered equipment downtime by 30% by providing faster access to maintenance information.

Customer Testimonials

Implementing this advanced document search solution has been a game-changer for our organization. The ability to handle vast amounts of documents with unparalleled speed and accuracy has significantly streamlined our operations. The advanced AI-driven search capabilities have not only enhanced our document retrieval processes but have also empowered our teams to work more efficiently. The robust security measures, including Role-Based Access Control, ensure that our sensitive information is well-protected, meeting the stringent standards required by federal agencies. This platform has truly transformed the way we manage and access our data, driving productivity and operational excellence across the board.
Chief Information Officer, Major Federal IT Services Provider

Leveraging OpenAI like Models for Modernizing Content Delivery and cutting Delivery Time from Weeks to hours with Generative AI Solutions

Mon, 23 Sep 2024 09:45:06 +0000

Leveraging OpenAI like Models for Modernizing Content Delivery and cutting Delivery Time from Weeks to hours with Generative AI Solutions

Revolutionizing Content Delivery with Generative AI: Achieving Faster Turnaround and Enhanced Efficiency

Company Overview

A leading company in the content marketing domain, this organization helps brands and their customers connect through high-quality, relevant content. The platform enables companies to produce and disseminate their content, improving customer acquisition, building brand loyalty, and sustaining interest in their offerings. The company also owns marketing analytics software that helps marketers compute ROI and improve outcomes across integrated digital and social channels.

Issue

Ensures content is consistent across channels with AI-driven solutions for uniform quality

AI handles routine tasks, freeing writers for creative work and scaling content production efficiently.

Quickly delivers quality content to support fast-paced campaigns, using AI to speed up the process.

Reduces revisions by producing near-final content, streamlining the approval process and eliminating delays.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

AI-Powered Content Generation

Integrated Generative AI models (OpenAI, Anthropic, Gemini) to rapidly generate high-quality content variants, reducing dependency on human writers.

Customizable Content Module

Developed a new platform feature where users input their content requirements, and AI generates multiple content options tailored to their needs.

Content Selection

Implemented analytics to assist users in selecting the most relevant, high-performing content variations.

Solution

Delivery Time Reduced from Weeks to Hours

Automation with AI slashed content creation times, enabling faster turnaround for campaigns.

Multiple Content Variants Generated

AI models produced various content versions, providing clients with a range of choices to best match their needs.

Substantial Cost Savings

Reduced reliance on human writers led to lower production costs, allowing for more content generation within the same budget.

Consistent Quality

Ensured high-quality content with fewer revisions, improving efficiency and client satisfaction.

Impact

Increased Efficiency & Scalability

Automated content creation significantly reduced production time while enabling the handling of higher content volumes, meeting market demands effortlessly.

Cost and Time Savings

AI-driven solutions minimized the need for human labor, cutting costs and speeding up content delivery.

Enhanced Customer Engagement

Faster delivery of high-quality content improved customer engagement, helping brands achieve their marketing goals more effectively.

Customer Testimonials

Our experience with the new AI-driven content generation platform has been transformative. The ability to reduce our content delivery time from weeks to just hours has had a profound impact on our efficiency and responsiveness. The integration of leading Generative AI models has allowed us to generate multiple high-quality content variations quickly, which has been instrumental in meeting tight deadlines and managing increased content demands. The significant cost and time savings, combined with the improved quality of content, have greatly enhanced our overall client satisfaction. This solution has truly revolutionized our content delivery process, setting a new standard for speed and quality in content marketing
Chief Content Officer, Leading Content Marketing Firm

Streamlining Large Volumes of Data for Campaign Management with Security

Mon, 23 Sep 2024 09:27:34 +0000

Streamlining Large Volumes of Data for Campaign Management with Security

Optimizing Data Flow and Ensuring Robust Security for Effective Campaign Execution

Company Overview

DemandScience is a data-driven marketing technology company focused on lead generation and campaign management. The company faced the challenge of integrating and processing large volumes of data exported from a third-party system, with the need for efficient handling, security, and real-time error notifications.

Issue

Multiple files with up to 20 columns required proper conversion and ingestion.

Large XLSX files needed conversion to CSV for further processing.

A fully automated pipeline was necessary to handle file ingestion, processing, and error notifications in real-time.

Data security and lifecycle management were essential for compliance and storage cost optimization.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Actions Taken

S3 Bucket Setup: Created secure storage with lifecycle management to handle data from third-party systems. This reduced manual intervention and optimized costs.

Lambda Automation: Automated file conversion and transfer, ensuring smooth processing of large datasets with real-time error logging.

Snowflake Integration: Ingested and processed data with automated tasks for duplication management, ensuring accurate campaign and lead tracking.

Error Handling & Notifications: Real-time email alerts for file ingestion failures and task errors, ensuring quick resolution.

PowerBI Dashboards: Enabled detailed, real-time reporting and insights through structured data in Snowflake.

Solution

Efficient Data Processing

Automated workflows process large data files without delays. Enhanced Security: Strong encryption and access controls ensure data protection.

Cost Savings

Optimized storage with lifecycle policies reduced long-term costs. Improved. Data Quality: Upsert scripts and hash functions minimized data duplication, ensuring consistency.

Scalability

Scalable solution that grows with increasing data volumes. Advanced Reporting: PowerBI dashboards offered real-time, actionable insights.

Impact

Cost Reduction:

Automation and optimized storage reduced operational expenses.

Advanced Analytics:

PowerBI integration enabled real-time, data-driven decisions.

Proactive Error Management:

Real-time notifications reduced downtime, ensuring continuity.

Customer Testimonials

AAIC's solution has revolutionized our data processing capabilities. The automation, combined with robust error handling and real-time notifications, ensures that our data is always accurate and available when we need it. The integration with PowerBI has empowered us with unparalleled insights into our campaigns and leads, driving better business decisions.
DemandScience

Automating AWS Architecture for a Leading Telecommunication Company Using Terraform and Ansible

Fri, 23 Aug 2024 18:17:10 +0000

Automating AWS Architecture for a Leading Telecommunication Company Using Terraform and Ansible

Automated AWS infrastructure provisioning and management, ensuring compliance, security, and scalability while reducing deployment times and human errors through CI/CD pipelines to support business growth.

Company Overview

A leading telecommunication company, this organization provides a wide range of services including mobile, broadband, and TV services. Known for its extensive network coverage and innovative solutions, the company serves millions of customers globally, ensuring reliable and high-quality communication services. The company continually invests in technology to enhance service delivery and customer experience.

Issue

Automating the provisioning and management of AWS infrastructure.

Ensuring compliance and security across the AWS environment.

Reducing manual intervention and minimizing human errors in infrastructure management.

Enhancing scalability and reliability of the cloud infrastructure.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Implemented Infrastructure-as-Code (IaC) with Terraform for automated AWS resource provisioning and management, alongside Ansible for consistent configuration and compliance across environments.

Set up automated CI/CD pipelines to streamline infrastructure deployments, minimizing manual intervention and accelerating deployment times.

Integrated AWS CloudTrail, AWS Config, and CloudWatch for continuous compliance, security monitoring, and automated alerting.

Impact

Automated Infrastructure Provisioning

Successfully automated AWS resource provisioning and management, ensuring a streamlined infrastructure process.

Compliance and Security

Achieved robust compliance and security across the AWS environment with minimal manual oversight.

Optimized Deployment Pipelines

Reduced deployment times and minimized human errors through fully automated CI/CD pipelines, enhancing scalability and reliability.

Customer Testimonials

Applied AI Consulting's expertise in Terraform and Ansible has been pivotal in automating our AWS infrastructure. Their solutions have significantly reduced manual intervention and minimized human errors, leading to a more secure and compliant environment.
Head of Engineering Leading Telecommunication Company

A Leading Insurance MNC Saves Costs with Infrastructure-as-Code Facilitated by AWS CloudFormation

Fri, 23 Aug 2024 18:01:56 +0000

A Leading Insurance MNC Saves Costs with Infrastructure-as-Code Facilitated by AWS CloudFormation

Reduced app deployment time from weeks to hours, achieved significant cost savings, and enhanced infrastructure flexibility and scalability using AWS CloudFormation and containerization.

Company Overview

A leading company in the insurance sector, this organization provides a comprehensive range of life insurance products, including individual and group insurance plans. With a strong focus on customer-centric solutions, the company offers protection, savings, pension, investment, health, and annuity products. Known for its innovative approach and commitment to quality, the company serves millions of customers, ensuring their financial security and peace of mind.

Issue

The customer was manually deploying apps, taking 5 to 6 weeks, slowing down the software development lifecycle (SDL).

Increasing costs due to lack of containerization, leading to inefficiencies in transitioning app environments from testing to development to production.

Manual IT infrastructure management was exhausting and time-consuming, leading to increased technical debt and slower development cycles.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Option Evaluation and Decision: Presented the client with two infrastructure provisioning and deployment options: Terraform and Helm or AWS CloudFormation. The client selected AWS CloudFormation due to team familiarity and future flexibility for modifications.

Containerization and DevOps Pipeline Implementation: Implemented containerization for a portable computing environment with VMs, and developed a DevOps pipeline to automate testing, integration, and deployment. This shortened the software development lifecycle (SDL) and enhanced collaboration between development and operations teams.

Legacy IT Infrastructure Modernization: Codified and managed the client’s legacy IT infrastructure using AWS CloudFormation, providing a robust Infrastructure-as-Code (IaC) solution.

Impact

Email Optimization

Accelerated Deployment: Reduced application deployment time from 5-6 weeks to just a few hours.

Productivity Boost

Cost Savings and Efficiency: Achieved significant cost savings through containerization and automated infrastructure management, and improved SDL efficiency with a robust DevOps pipeline, leading to faster, high-quality app releases.

Scalable Infrastructure

Enhanced Infrastructure Management: Improved flexibility and scalability of IT infrastructure with AWS CloudFormation.

Customer Testimonials

Thanks to Applied AI Consulting, our IT infrastructure is now more flexible and scalable, supporting our growth and innovation goals. Their deep knowledge of AWS CloudFormation and Infrastructure-as-Code has truly elevated our operational capabilities.
Head of Engineering Leading Insurance MNC

Accelerating Integration with a Robust API Platform: How a Leading Automation Provider Transformed Their Processes Using AWS Services

Fri, 23 Aug 2024 16:28:07 +0000

Accelerating Integration with a Robust API Platform: How a Leading Automation Provider Transformed Their Processes Using AWS Services

Streamlining Operations: Leveraging AWS to Enhance Automation Through a Powerful API Platform

Company Overview

A leading company in the automation platform domain, this organization provides innovative solutions for enterprise automation, including digital transformation, process improvement, and growth strategies. Their platform addresses complex challenges such as information access, approval processes, high transaction volumes, and compliance issues. The company’s integrated payment solutions and process automation tools are revolutionizing enterprises by making operations more efficient and streamlined.

Issue

Time-Consuming Integrations: Building and maintaining point-to-point integrations for each client was resource-intensive and inefficient.

Lengthy Integration Process: Integrating with third-party ERPs and payment systems took 8-12 weeks per client, delaying project timelines.

Limited Scalability: The existing process constrained the ability to serve multiple clients simultaneously, limiting growth.

Need for a Scalable Solution: A scalable, reusable integration framework was required to streamline and accelerate the onboarding process for new clients.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

API-Based Integration Gateway: Implemented a robust API-based Integration Gateway using AWS services like Amazon API Gateway, AWS Lambda, and others to streamline integrations.

Custom Solution Architecture: Designed a four-component architecture (Flow, Filter, Transformer, Adopter) to efficiently manage and transform inputs for various ERP systems, enhancing compatibility and flexibility.

Automated CI/CD Pipelines: Established automated CI/CD pipelines to ensure continuous integration and deployment, minimizing manual intervention and enhancing system reliability.

Impact

Accelerated Time-to-Market & Customer Onboarding

Significantly improved the accuracy and efficiency of email content classification and follow-ups. Reduced integration and deployment times by 80%, enabling faster rollout of new features and rapid onboarding of new clients in under a week.

Operational Efficiency & Cost Savings

Automated over 30 tasks and implemented a reusable integration framework, leading to a 60% increase in efficiency and a 40% reduction in operational costs.

Scalability & Enhanced User Experience

Scalable architecture supported increased workloads and customer demands, while a simplified, user-friendly interface improved client satisfaction and expanded customer capacity by 80%.

Customer Testimonials

The API platform developed by Applied AI Consulting has been a game-changer for us. It has significantly reduced manual efforts and errors, enabling us to onboard 80% more customers with ease. Their work has truly elevated our capabilities.
CTO 7Targets.ai

Accelerating Private Sector Bank’s App Releases with AWS and DevOps Expertise by Applied AI Consulting

Fri, 23 Aug 2024 16:15:05 +0000

Accelerating Private Sector Bank’s App Releases with AWS and DevOps Expertise by Applied AI Consulting

Streamlining Development and Deployment Pipelines for Faster, Secure, and Scalable Banking Solutions

Company Overview

One of the leading private sector banks in India, this financial institution provides a wide array of services including retail banking, corporate banking, and wealth management. With a commitment to customer-centric innovation, the bank offers an extensive range of products such as savings accounts, loans, fixed deposits, and credit cards. The bank serves millions of customers across the country through its extensive network of branches and digital banking platforms. Known for its robust infrastructure and innovative solutions, the bank continuously strives to enhance customer experience and operational efficiency.

Issue

Slow App Release Cycle: The bank struggled to release apps quickly, hindered by the manual management of its IT infrastructure.

Legacy System Bottlenecks: Outdated infrastructure caused extended software development cycles and made it difficult to maintain high app quality.

Inefficient IT Management: The centralized, manually operated IT infrastructure was not only time-consuming but also prone to errors and costly to maintain.

Technical Debt: The accumulation of technical debt due to outdated coding practices stifled innovation and reduced the bank’s ability to be agile.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

CI/CD Pipelines: Enabled faster and more reliable deployments by automating the software release process.

Infrastructure-as-Code: Utilized Terraform to streamline deployment speed, cut costs, and reduce errors through automated infrastructure management.

App Containerization: Enhanced scalability and security, accelerating the time to market and improving overall application performance.

Impact

Faster App Releases

Implementing CI/CD pipelines reduced the app release cycle time by 40%, allowing the bank to quickly roll out new features and updates to meet customer demands.

Enhanced Reliability and Security

By employing Infrastructure-as-Code with Terraform and app containerization with AWS ECS, the bank achieved a 99.9% uptime, reducing the risk of downtime and data breaches.

Cost Efficiency

Automation of manual processes and reduced technical debt led to a 30% decrease in operational costs, allowing the bank to allocate resources more effectively.

Customer Testimonials

The collaboration with Applied AI Consulting has revolutionized our approach to infrastructure management. Their strategic implementation of CI/CD and containerization has drastically improved our time-to-market, while also reducing technical debt. The automation of our infrastructure has led to unprecedented levels of efficiency and reliability. This partnership has been crucial in our journey towards a more agile and innovative banking platform.
VP of Engineering Leading Private Sector Bank

Enhancing Deployment Success with Python Selenium Automation for a Leading Content Marketing Platform

Fri, 23 Aug 2024 15:50:57 +0000

Enhancing Deployment Success with Python Selenium Automation for a Leading Content Marketing Platform

Company Overview

A leading company in the content marketing domain, this organization helps brands and their customers connect through high-quality, relevant content. The platform enables companies to produce and disseminate their own content, improving customer acquisition, building brand loyalty, and sustaining interest in their offerings. Known for its innovative approach and commitment to quality, the company serves a diverse clientele, including top brands and enterprises, ensuring their content marketing strategies are effective and impactful.

Issue

Ensuring consistent and reliable deployments across multiple environments.

Reducing manual testing efforts and increasing the efficiency of the QA process.

Automating repetitive tasks to free up resources for more strategic activities.

Enhancing the overall quality and performance of their content marketing platform.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Streamlined UI testingby implementing Python Selenium for automation, enhancing efficiency and reducing manual testing efforts.**

Developed automated test scriptsto validate critical functionalities across various environments, ensuring consistent performance.

Integrated automation with CI/CD pipelines** for continuous testing and deployment, along with providing training and documentation for seamless adoption by development and QA teams.

Impact

Increase in Deployment Success

Significantly reduced failed deployments and rollbacks, enhancing reliability in the deployment process.

Reduction in Manual Testing

Freed up the QA team to focus on complex tasks, driving efficiency and strategic improvements.

Enhanced Quality and Customer Satisfaction

Improved platform performance and reliability, supporting faster, more dependable releases aligned with company growth and innovation goals.

Customer Testimonials

Applied AI Consulting’s mastery in Python Selenium automation was a game-changer for us. Their ability to integrate seamlessly with our CI/CD pipeline drastically improved our deployment success rate by 50%, while cutting down manual testing by 70%. Their innovative approach and commitment to excellence have significantly elevated our platform's performance, enabling us to deliver faster and more reliable releases.
CTO marxeed.com

Building a Secure and Scalable Enterprise Document Search Platform for a Major Federal IT Services Provider

Fri, 23 Aug 2024 15:49:08 +0000

Building a Secure and Scalable Enterprise Document Search Platform for a Major Federal IT Services Provider

Leveraging Advanced Technologies to Enhance Document Retrieval, Security, and Scalability in Complex IT Environments

improved customer engagement and response rates

Company Overview

A major company in the IT consulting and services domain, this organization specializes in delivering premium solutions to USA federal agencies and enterprises. Known for their expertise in security, scalability, and availability, the company provides cutting-edge technology solutions that cater to the complex needs of their clients. Their services include consulting, managed services, and custom IT solutions.

Issue

Addressing the demand for a secure and scalable document search platform capable of supporting one of the largest US federal agencies.

Ensuring the platform’s ability to efficiently manage and search through petabytes of documents with both speed and accuracy.

Implementing robust Role-Based Access Control (RBAC) to uphold stringent security standards.

Integrating advanced AI techniques to enable contextual and generative search functionalities.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Collaborated with the customer to design and build a secure, scalable enterprise document search platform using the Azure ecosystem, incorporating proprietary technology for efficient document crawling and embedding.

Implemented Mistral managed service for robust management of AI models and data, and developed a RAG (Retrieval-Augmented Generation) approach to enhance the search experience with generative AI.

Ensured the platform’s compliance with the stringent security, scalability, and availability requirements of a federal agency

Impact

Platform Development

Successfully created a secure and scalable document search platform tailored for a federal agency, capable of managing large-scale data.

High-Speed Search

Enabled the efficient processing and retrieval of petabytes of documents, ensuring rapid and accurate search capabilities.

Security & AI

Integrated RBAC and generative AI for enhanced security and user experience.

Customer Testimonials

The enterprise document search platform has revolutionized our operations, delivering exceptional security, scalability, and speed. Its advanced AI and robust access control have significantly improved our efficiency and document management.
VP of Engineering

Facilitating DevOps with Application Modernization & Architecture Reboot for a Leading Content Marketing Platform

Fri, 23 Aug 2024 15:29:44 +0000

Facilitating DevOps with Application Modernization & Architecture Reboot for a Leading Content Marketing Platform

Boosting Deployment Efficiency, Scalability, and Cost Savings While Enhancing Security

Company Overview

A leading company in the content marketing domain, this organization helps brands and their customers connect through high quality, relevant content. The platform enables companies to produce and disseminate their own content, improving customer acquisition, building brand loyalty, and sustaining interest in their offerings. The company also owns a marketing analytics software that helps marketers compute ROI and improve outcomes across integrated digital and social channels.

Issue

Need for migration and modernization to handle peak load efficiently

Challenges in deployment, maintenance and support existing applications

Need a revamp existing infrastructure to enable automation and DevOps, supporting continuous upgrades

Let’s discuss your use cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Similar Use Cases

Solution

Containerization and Orchestration: Containerized applications with Docker and deployed them in Kubernetes (K8s) for enhanced management, scalability, and resilience.

Automated Deployment and Testing: Used GitHub Actions for CI/CD automation, AWS CloudFormation for Infrastructure as Code, and Selenium and OWASP for automated and security testing.

Monitoring, Alerts and Cost Optimization: Implemented CloudWatch for monitoring and alerts, integrated GitHub and Slack for CI/CD notifications, and utilized AWS Lambda for efficient resource management and cost savings.

Impact

Scalability and Performance

Containerization and auto-scaling enabled seamless handling of increased traffic, significantly improving peak load management.

Deployment Efficiency

Automated CI/CD pipelines reduced deployment time from several hours to minutes, streamlining release cycles and minimizing downtime.

Cost Optimization

Enhanced scalability, reliability, and security of applications, achieving significant cost savings through resource optimization.

Customer Testimonials

Applied AI Consulting transformed our platform with modernized IT infrastructure and DevOps practices. Their expertise improved our deployment speed, scalability, and security, significantly reducing costs and boosting our ability to connect with customers. They've been an invaluable partner in our digital transformation.
Head of Marketing Marxeed

Achieving Scalability and Efficiency: Going Serverless with AI-Based Email Assistant for a Leading Sales Tech Platform

Fri, 23 Aug 2024 15:04:16 +0000

Achieving Scalability and Efficiency: Going Serverless with AI-Based Email Assistant for a Leading Sales Tech Platform

Going Serverless with AI-Based Email Assistant for a Leading Sales Tech Platform

Company Overview

A leading company in the sales technology sector, this organization focuses on improving sales efficiency and productivity using its patented AI-based lead nurturing solution. The company’s flagship product, AI Sales Assistant, utilizes natural language processing (NLP) and machine learning (ML) to automate email communications, follow-ups, and response categorization, significantly enhancing sales performance.

Issue

Ensuring the ability to send emails evenly distributed over time to avoid sudden spikes.

Accurately reading received emails and determining the next action to be taken by the AI Sales Assistant after proper categorization.

Extracting entities such as dates, phone numbers, and titles from the received emails.

Setting up a continuous deployment pipeline with proper testing to ensure zero downtime.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Automated Lead Processing and Response Handling: Developed state machines using AWS Step Functions and Lambda functions to streamline the lead processing workflow, including email validation, sentiment analysis, and property extraction.

Seamless Integration and Email Management: Configured AWS SES to receive and store emails in S3, and integrated with third-party CRMs like Hubspot and ZohoCRM for efficient lead management.

Scalable and Reliable Deployment: Implemented automated, zero-downtime deployments to QA and production environments using a suite of AWS services, ensuring high availability and security for the email assistant platform.

Impact

Increased Email Sending Capacity

Scaled email sending capability from 1,000 to 10,000 emails per day, accommodating larger volumes and improving outreach efficiency.

Enhanced Email Response Management

Improved processing and categorization of email responses to ensure accurate and timely follow-up actions, boosting overall response quality.

Implemented Advanced Deployment Strategies

Achieved auto-scaling to handle increased email volumes and implemented automated zero downtime deployments, enhancing system reliability and availability.

Customer Testimonials

Applied AI Consulting's deep knowledge in AI-driven solutions, especially in machine learning and natural language processing, played a key role in optimizing our email assistant’s performance. Their strategic serverless AWS implementation has not only ensured scalability but also delivered remarkable efficiency gains, greatly benefiting our end-users.
CTO 7Targets.ai

Streamlining Development Operations: Migrating from GitLab to GitHub for a Leading Content Marketing Platform

Fri, 23 Aug 2024 11:20:11 +0000

Streamlining Development Operations: Migrating from GitLab to GitHub for a Leading Content Marketing Platform

Optimizing Development Operations by Migrating from GitLab to GitHub for Enhanced Efficiency and Cost Savings

Company Overview

Issue

The existing GitLab solution was costly, impacting the budget for development operations.

GitLab’s integration with other tools in the development pipeline was insufficient.

The need for a more scalable version control system to handle increased workloads.

Ensuring a smooth migration from GitLab to GitHub with minimal disruption to ongoing projects.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Migration Execution: Planned and executed the migration to GitHub, transferring all repositories, issues, and CI/CD pipelines accurately.

Enhanced Tool Integration: Integrated GitHub with Jira for project management and Slack for communication and also integrated GitHub Actions for automating build, test, and deployment processes.

Training and Support: Conducted training sessions for the development team to ensure effective use of GitHub’s features.

Impact

Operational Cost Savings

Migration to GitHub reduced operational costs through its cost-effective pricing model.

Improved Scalability

GitHub’s scalable infrastructure effectively managed increased workloads, supporting business growth.

Increased Productivity

Integration with various collaboration tools and streamlined workflows boosted team productivity.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Building a Hybrid Payment Platform from Scratch on AWS Stack

Fri, 23 Aug 2024 11:18:02 +0000

Building a Hybrid Payment Platform from Scratch on AWS Stack

Company Overview

A leading company in the payment processing industry, this organization specializes in providing secure and efficient payment solutions for businesses of all sizes. Known for its innovative approach, the company offers a comprehensive range of services, including online payment gateways, mobile payments, and point-of-sale systems. With a focus on reliability and customer satisfaction, the company serves a diverse clientele, ensuring seamless and secure transactions.

Issue

Ensuring the platform could scale to handle increasing transaction volumes.

Implementing robust security measures to protect sensitive payment data.

Integrating with multiple payment gateways and financial institutions, like Stripe, Plaid etc.

Maintaining high availability and reliability.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Architected a hybrid payment platform on AWS ECSwith auto-scaling capabilities to manage fluctuating transaction volumes efficiently, leveraging AWS Lambda and AWS Fargate for serverless operations to minimize operational overhead.

Implemented robust security measuresutilizing AWS IAM for access control and encryption for data protection both at rest and in transit, ensuring compliance and safeguarding sensitive financial information.

Integrated multiple payment gateways and financial institutions via APIs and established CI/CD pipelines to automate updates and maintenance, ensuring seamless and efficient deployment processes.

Impact

Scalable and Secure hybrid payment

Ensured reliable transaction processing with high availability and minimal downtime.

Security Measures

Safeguarded sensitive payment data, maintaining robust protection against potential threats.

Streamlined operations

Through CI/CD pipelines, leading to more efficient and continuous delivery of updates.

Implementing a Secure and Scalable Infrastructure on AWS for a Leading Tech Hiring Platform

Fri, 23 Aug 2024 11:12:05 +0000

implementing-a-secure-and-scalable-infrastructure-on-aws-for-a-leading-tech-hiring-platform

Leveraging AWS to Achieve High Availability, Security, and Performance in Tech Recruitment Operations

Company Overview

A leading company in the tech hiring domain, this organization provides a platform that connects top talent with companies, facilitating faster and more efficient hiring processes. The platform leverages Artificial Intelligence (AI), gamification, and trusted networks to find the right professionals. Known for its innovative approach, the company helps businesses execute their vision by finding and engaging with the best talent.Known for their innovative approach and customer-centric solutions, the company serves a diverse clientele in the financial services sector.

Issue

Implementing a secure and scalable infrastructure on AWS.

Ensuring high availability and reliability of the platform.

Integrating with various talent management systems and engaging with top talent.

Automating deployment processes to reduce downtime and improve efficiency.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Optimized Container Management and Traffic Distribution: Utilized AWS ECS for efficient container orchestration and AWS Load Balancers to ensure even distribution of incoming traffic, enhancing platform performance and scalability.

Enhanced Security and Monitoring: Implemented AWS WAF to safeguard the platform from web exploits, set up AWS CloudWatch for comprehensive monitoring and logging, and used AWS SNS for reliable messaging and notifications.

Streamlined Deployment Processes: Employed CodeDeploy and GitHub Actions for CI/CD, automating deployment workflows to reduce downtime and ensure seamless updates.

Impact

Secure and Scalable Infrastructure

Successfully implemented a robust AWS infrastructure that ensures scalability and security, supporting the growing needs of the tech hiring platform.

High Availability and Reliability

Achieved continuous service availability with minimal downtime, providing a reliable user experience and maintaining operational consistency.

Enhanced Security and Performance

Improved platform security by protecting against common web exploits and attacks, while enhancing deployment efficiency to boost overall system performance.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Enhancing Communication Categorization and Response for a Leading Loan Recovery Platform Using AI

Thu, 22 Aug 2024 16:28:34 +0000

Enhancing Communication Categorization and Response for a Leading Loan Recovery Platform Using AI

Optimizing Email Management and Automation for Improved Efficiency and Accuracy

Company Overview

A leading company in the loan recovery platform domain, this organization specializes in managing and recovering loans for financial institutions. Their platform facilitates the efficient categorization and response to customer communications, ensuring timely interactions that maximize recovery rates. Known for their innovative approach and customer-centric solutions, the company serves a diverse clientele in the financial services sector.

Issue

High Communication Volume: The customer manages 12,000 daily communications from diverse channels, including SMS, website, emails, calls, chats, and social media.

Inefficient Manual Processing: Manual categorization and response to these communications were time-consuming, frequently resulting in delays of several days.

Critical Timeliness: Prompt responses are essential for making timely offers or discounts to optimize recovery efforts.

Need for Automation: An automated solution was required to categorize communications and provide real-time responses efficiently.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Custom Machine Learning Model: Developed a bespoke machine learning classification model to categorize communications into 250 distinct categories, enhancing sorting accuracy and efficiency.

Automated Categorization with NLP: Leveraged natural language processing (NLP) to automate the categorization process, streamlining communication management and reducing manual effort.

Real-Time Action Integration: Implemented real-time actions to send emails, SMS, or engage through other mediums, and seamlessly integrated the solution with existing communication channels for uninterrupted operation.

Impact

Significant Reduction in Response Time

Cut response time from several days to a few hours, ensuring quicker engagement with customers.

High Auto-Response Efficiency

Achieved a 60% auto-response rate using AI NLP, with the remaining 40% managed through standard processes on the same day.

Enhanced Recovery and Engagement

Boosted customer engagement and recovery rates by implementing timely, effective responses, leading to improved overall efficiency in the loan recovery process.

Revolutionizing Marketing Campaign Analysis with Generative AI for a Leading Financial Services Engagement Platform

Thu, 22 Aug 2024 16:09:56 +0000

Revolutionizing Marketing Campaign Analysis with Generative AI for a Leading Financial Services Engagement Platform

Leveraging Generative AI to Transform Data Insights and Optimize Marketing Strategies for Enhanced Engagement and Performance

Company Overview

A leading company in the financial services engagement platform domain, this organization provides innovative solutions for enhancing customer experiences. Their platform enables financial institutions to design, execute, and analyze marketing campaigns, ensuring high-quality interactions and impactful outcomes. Known for their cutting-edge technology, the company serves a diverse clientele, helping financial services businesses achieve their marketing goals.

Issue

Difficulty in analyzing large volumes of campaign data efficiently.

Challenges in navigating and synthesizing numerous data points, tables, reports, and dashboards to evaluate campaign performance.

The traditional data analysis process was cumbersome and time-consuming, requiring significant manual effort to extract relevant insights.

The absence of natural language query capabilities hindered the ability to quickly access and interpret data, tables, graphs, and charts.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Generative AI-Based Solution: Developed a cohort analysis tool powered by Generative AI to better analyze campaign data.

Natural Language Processing Integration: Implemented NLP to enable marketing teams to query data using natural language, simplifying data retrieval.

Real-Time Data Integration: Integrated the solution with existing data sources to provide accurate, real-time responses and a user-friendly dashboard with various data visualization formats.

Impact

Accelerated Decision-Making

Integration with Generative AI and NLP significantly reduced the time needed to obtain actionable insights, enabling faster and more informed decision-making for campaign adjustments.

Cost and Efficiency Gains

Automation of data analysis and the user-friendly interface cut operational costs and improved efficiency, reducing manual effort and allowing the marketing team to concentrate on strategic initiatives.

Enhanced Campaign Effectiveness

Access to real-time, accurate data and advanced insights led to improved campaign performance, higher productivity, and better overall results, setting the platform apart from competitors.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Automating Rule Generation with Generative AI for a Leading Offer Management Platform in Financial Services.

Thu, 22 Aug 2024 11:27:45 +0000

Automating Rule Generation with Generative AI for a Leading Offer Management Platform in Financial Services

Accelerating Code Generation, Boosting Accuracy, and Streamlining Payout Processes for Enhanced Efficiency in Financial Offer Management

Company Overview

A leading company in the offer management platform domain for banks and financial institutions, this organization provides innovative solutions to streamline the verification and fulfillment of offers. Their platform enhances customer engagement by managing offers efficiently and ensuring compliance and accuracy in payouts. Known for their cutting-edge technology, the company serves a diverse clientele, helping financial services businesses achieve their marketing and operational goals.

Issue

The process of manually writing JavaScript code to verify offer fulfillment was inefficient, leading to time-consuming tasks and a high risk of errors.

The lack of an automated solution to generate JavaScript code based on customer and offer metadata created bottlenecks in the workflow.

The accuracy and reliability of the manually generated code were inconsistent, potentially causing delays in payouts.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Developed a Generative AI-based solution that automated the generation of JavaScript code, significantly reducing manual effort and errors.

Implemented natural language processing (NLP) to enable the system to accurately interpret customer and offer metadata for precise code generation.

Integrated the AI solution with the existing offer management platform, ensuring real-time and accurate code output. Designed a user-friendly interface to make the process intuitive and accessible for the customer success team.

Impact

Accuracy and Reliability

Enhanced the accuracy and reliability of the generated code.

Productivity Boost

Significantly reduced the time required to generate and verify offer fulfillment code and hence improved the overall efficiency of the customer success team.

Faster Payouts

Enabled quicker and more accurate payouts based on verified offer fulfillment.

Enhancing Customer Support with AI-Driven Agent Assistance for a Leading InsureTech Provider

Thu, 22 Aug 2024 11:23:58 +0000

Enhancing Customer Support with AI-Driven Agent Assistance for a Leading InsureTech Provider

Boost Customer Support Efficiency with AI-Enhanced Real-Time Assistance and Improved Accuracy

Company Overview

A leading company in the InsureTech domain, this organization specializes in providing innovative solutions to enhance customer support and operational efficiency. They focus on leveraging advanced technologies to streamline processes and improve customer interactions. Known for their commitment to quality and customer satisfaction, the company serves a diverse clientele in the insurance sector.

Issue

Integrate AI to identify claim scenarios and provide real-time suggestions during calls.

Offer agents real-time guidance on required actions and attributes for effective claim handling.

Automate updates to customer and case information based on input received during calls.

Allow agents to verify details and access downloadable call recordings, transcripts, and summaries for thorough review.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Enhanced Call Management: Developed an Agent Assist feature allowing agents to dial numbers directly from the Connect Control Panel (CCP) and integrated Amazon Contact Lens for scenario identification and real-time transcription with Amazon Transcribe.

AI-Powered Assistance: Utilized Amazon Wisdom to suggest required attributes and actions, and ensured automatic updates to customer and case information in the CCP based on call insights.

Real-Time Analytics & Documentation: Enabled real-time analytics and reporting with AWS Cloud services, and provided agents with options to download call recordings, transcripts, and summaries for in-depth review.

Impact

Real-Time Assistance & Accuracy

Enhanced agent support with real-time identification of claim scenarios and actionable suggestions, improving accuracy and efficiency.

Faster Processing

Enabled quicker and more reliable processing of customer queries and claims, boosting overall operational efficiency.

Comprehensive Records & Focused Support

Provided detailed call records and summaries, allowing agents to concentrate on complex interactions and further analysis.

Driving Quality and Reliability for Modern Mobile and Web Applications

Wed, 21 Aug 2024 15:26:41 +0000

Driving Quality and Reliability for Modern Mobile and Web Applications

Building a Modern Mobile and Web Application with Robust CI/CD Automation and AI-Powered Multi-Device Testing

Company Overview

A leading start-up in the online consulting platform domain, this company focuses on connecting end-users with top industry professionals for expert consultation services. Known for its innovative approach and cutting-edge technology, the company aims to provide seamless and efficient solutions for both consultants and clients, ensuring high-quality interactions and problem-solving experiences. Applied AI Consulting has created the modern stack with payment gateway integration for this customer. This case study is focused on quality engineering.

Issue

Cross-Platform Compatibility: Building a modern application that supports both mobile and web platforms can introduce challenges in ensuring consistent functionality and user experience.

Testing Coverage: Ensuring thorough and reliable UI and API testing to cover all possible use cases and scenarios, which is crucial for maintaining application quality and reliability.

Automation Integration: Integrating automation testing into the CI/CD pipeline effectively, ensuring that automated tests run seamlessly with each deployment to catch issues early.

Performance and Reliability: Maintaining high performance and reliability of the application across multiple platforms and under varying load conditions.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Developed and Integrated Automation Frameworks: Created a Selenium-Python-based framework for UI testing and a pyTest framework for API testing, integrating them with the CI/CD pipeline.

Executed Cross-Platform Testing: Utilized aiTest for automation across multiple devices, browsers, and versions, including API quality validation with Postman collections.

Enhanced Communication and Visibility: Provided test execution updates and analytics through Slack and email, and demonstrated automation results in end-of-iteration demos.

Impact

Enhanced Quality and User Experience

Achieved very high application quality with minimal defects and ensured a seamless user experience across devices and browsers.

Accelerated Release Cycles

Reduced testing time by 40% through CI/CD integration and automation, leading to faster feature releases and updates.

Cost Savings and Performance Improvement

Decreased testing-related costs by 30% and improved application performance, contributing to higher user retention and scalable growth.

Customer Testimonials

Applied AI Consulting has been instrumental in driving the quality and reliability of our mobile and web applications. Their expertise in building a modern stack with seamless payment gateway integration has allowed us to provide our users with a smooth and efficient experience. The team's commitment to excellence and their innovative approach have significantly enhanced our platform's performance, ensuring we meet the high standards our clients expect.
Founder Click to Connect

Reduced Operational Cost by 70%: Migrating from Rundeck to AWS Batch for a Leading Content Marketing Platform

Wed, 21 Aug 2024 15:01:07 +0000

Reduced Operational Cost by 70%: Migrating from Rundeck to AWS Batch for a Leading Content Marketing Platform

Transforming Cost Efficiency: How Migrating to AWS Batch Slashed Operational Costs by 70% for a Content Marketing Leader

Company Overview

Issue

High Operational Costs: Elevated costs associated with using Rundeck for batch processing.

Need for Scalability: Requirement for a more scalable and efficient solution to manage growing workloads.

Migration Disruption: Ensuring minimal disruption during the transition from Rundeck to the new solution.

Performance Enhancement: Improving the overall performance and reliability of batch processing operations.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Migration to AWS Batch Movedbatch processing operations from Rundeck to AWS Batch, leveraging AWS’s scalable infrastructure for enhanced performance and efficiency.**

Automation & Orchestration **Implemented AWS Lambda for job management and AWS Step Functions for orchestrating complex workflows and managing job dependencies.

Monitoring & Transition Utilized AWS CloudWatch for monitoring and alerting, ensuring smooth operations and a seamless transition with minimal downtime through meticulous planning.

Impact

Cost Savings & Efficiency

Migrating to AWS Batch reduced operational costs by 70%and automated batch processing, cutting job execution time by40%

Scalability & Reliability

Leveraged AWS Batch’s scalable architecture to efficiently handle increased workloads, supporting effective business growth

Resource Optimization

Reduced manual intervention and improved operational efficiency, enabling better allocation of resources to strategic initiatives.

Modernizing Meter Data Analytics with Machine Learning for a Leading Utility Provider

Wed, 21 Aug 2024 14:53:06 +0000

Modernizing Meter Data Analytics with Machine Learning for a Leading Utility Provider

Enhanced operational efficiency by optimizing anomaly detection and circuit balancing, unlocked data silos for better utilization, improved customer engagement through actionable analytics, and ensured robust security and compliance with industry standards.

Company Overview

A leading company in the utility domain, this organization specializes in the distribution of electricity and gas. They are committed to delivering reliable and efficient energy services to their customers. Known for their innovative approach and dedication to customer service, the company continually seeks to leverage advanced technologies to improve their operations and customer engagement.

Issue

The need to build a modern data analytics solution to improve the availability and usability of meter data for operational and customer insights.

Unlocking data silos and using the right data stores and analytics tools for various tasks.

Detecting meter and distribution circuit anomalies, running circuit balancing, thwarting energy theft, and predicting demand.

Enhancing customer engagement with proactive, meaningful analytics, forecasts, and predictions.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Comprehensive Data Analytics Solution: Built a modern data analytics solution using AWS services, including Amazon S3, Redshift, EMR, Athena, SageMaker, QuickSight, and Managed Grafana.

Unified Data Integration: Integrated diverse data sources, such as CIS, weather data, and MDMS, into a unified data lake, and utilized AWS Glue for ETL processes.

Advanced Machine Learning and Insights: Implemented machine learning models with Amazon SageMaker for anomaly detection and demand prediction, and created dashboards and reports with QuickSight and Managed Grafana for actionable insights while ensuring robust security and compliance.

Impact

Enhanced Meter Data Availability

Improved the availability and usability of meter data, providing valuable operational and customer insights.

Optimized Operations and Resource Allocation

Successfully detected meter and distribution circuit anomalies, thwarted energy theft, and predicted demand accurately to enhance operational efficiency and optimize resource allocation.

Boosted Customer Engagement

Enhanced customer engagement through proactive and meaningful analytics, forecasts, and predictions.

Ensuring Performance and Scalability for Demand Generation Tech with Advanced Automation Testing

Wed, 21 Aug 2024 14:37:36 +0000

Ensuring Performance and Scalability for Demand Generation Tech with Advanced Automation Testing

Company Overview

A leading company in the demand generation technology sector, this organization provides data-driven marketing solutions to help businesses drive revenue growth. With a comprehensive suite of services, including lead generation, data management, and analytics, the company supports its clients in enhancing their marketing effectiveness. Known for its innovative approach and robust technology infrastructure, the company continuously seeks to improve its product offerings and operational efficiency.

Issue

Create a TestNG Maven framework for UI automation and RestAssured for API automation.

Perform functional UI and API automation and performance testing (load testing) to support 25,000 concurrent users on API calls and 5,000 concurrent users on UI with 30 million plus records in the database.

Integrate automation with the CI/CD pipeline.

Maintaining high availability and reliability.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Accelerated multi-browser testing and load testingby leveraging AAIC’s aiTest, ensuring comprehensive UI and backend performance validation.**

Developed a robust TestNG Maven frameworkwith Selenium and Java for UI automation, and RestAssured for API automation, enabling efficient and scalable testing.

Drastically reduced build qualification time** by implementing parallel test runs, cutting UI test time to 9 minutes and API test time to just 2-3 minutes.

Impact

Comprehensive Framework

Led by a QA lead, AAIC’s 5-member QA team architected and implemented a robust testing framework, covering functional UI and API automation, and enabling large-scale load testing.

High-Scale Load Testing

Successfully conducted load tests for 25,000 concurrent API users and 5,000 concurrent UI users, with a database of over 30 million records, ensuring the application’s performance and scalability under heavy load.

Seamless CI/CD Integration

Integrated automation with the CI/CD pipeline, enabling rapid and efficient build qualification tests, boosting confidence in the application’s readiness for deployment.

Mitigating Vulnerabilities for a Leading Construction Logistics Platform

Wed, 21 Aug 2024 14:36:38 +0000

Mitigating Vulnerabilities for a Leading Construction Logistics Platform

Mitigated vulnerabilities, enhanced platform security with robust monitoring, and ensured minimal disruption during the process to maintain business continuity.

Company Overview

A leading company in the construction logistics domain, this organization provides innovative solutions to streamline trucking operations. Their platform connects contractors, fleet owners, and material producers, facilitating efficient scheduling, dispatching, and tracking of construction materials. Known for its robust technology and customer-centric approach, the company aims to enhance productivity and reduce operational costs in the construction industry.

Issue

Immediate need to address the critical Log4j vulnerabilities in their systems.

Ensuring minimal disruption to ongoing operations while mitigating the vulnerabilities.

Enhancing the security posture of their platform to prevent future vulnerabilities.

Implementing a comprehensive monitoring and alerting system to detect and respond to security threats promptly.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Option Evaluation and Decision: Presented the client with two infrastructure provisioning and deployment options: Terraform and Helm or AWS CloudFormation.

Containerization and DevOps Pipeline Implementation: containerization for a portable computing environment with VMs, and developed a DevOps pipeline to automate testing, integration, and deployment.

Legacy IT Infrastructure Modernization: Codified and managed the client’s legacy IT infrastructure using AWS CloudFormation, providing a robust Infrastructure-as-Code (IaC) solution.

Impact

Faster Threat Response

Reduced threat detection and response times by 50% through real-time monitoring and automated remediation

Enhanced Security and Compliance

Improved overall security and ensured compliance with industry standards, addressing Log4j vulnerabilities.

Operational Resilience and Cost Savings

Maintained business continuity with minimal disruption and reduced the risk of future vulnerabilities, potentially saving up to 40% in remediation costs.

Customer Testimonials

AAIC has been fantastic to work with. They're proactive and always spot opportunities for improvement. Their attention to detail and commitment to excellence make our collaboration truly rewarding.
Eric Johnson, Director, DevOps truxnow.com

Building a Comprehensive Online Consulting Platform with Payment Integration for a Leading Start Up

Wed, 21 Aug 2024 12:48:08 +0000

Building a Comprehensive Online Consulting Platform with Payment Integration for a Leading Start-Up

Enhancing User Experience with Secure Payments, Real-Time Notifications, and Automated Deployments

Company Overview

Issue

Ensuring high availability and scalability of the mobile and web applications.

Integrating a reliable and secure payment gateway.

Automating the deployment processes to reduce downtime and improve efficiency.

Maintaining high performance and user experience during peak loads.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Successfully deployed the ClickToConsult mobile application on both the App Store and Play Store, leveraging Firebase for push notifications and AWS Lambda for backend processes, ensuring scalability and cost efficiency.

Implemented a robust web-based interface for managing administrative activities, with integrated Razorpay payment gateway for secure transactions, providing a seamless experience for users and administrators.

Set up CI/CD pipelines for automated deployments across the mobile and web platforms, minimizing downtime and facilitating smooth, efficient updates.

Impact

Launch of a Scalable Consulting Platform

Successfully launched a scalable and high-performing online consulting platform, ensuring seamless interactions and enhanced user experience.

Secure Payments and User Trust

Integrated a secure and reliable payment gateway, boosting user trust and satisfaction through secure transactions.

Cost Efficiency with Serverless Architecture

Achieved significant cost savings and operational efficiency by utilizing serverless architecture and automated deployments.

A USA-Based Fintech Startup Achieved Faster Releases and Scalability Through AAIC’s DevOps Pipeline and Infrastructure-as-Code

Wed, 21 Aug 2024 12:45:56 +0000

A USA-Based Fintech Startup Achieved Faster Releases and Scalability Through AAIC’s DevOps Pipeline and Infrastructure-as-Code

Boost Speed and Scale with AAIC’s DevOps and IaC

Company Overview

A leading company in the fintech sector, this organization specializes in providing innovative financial technology solutions to streamline and enhance payment processing for businesses. Known for its cutting-edge approach and customer-centric services, the company offers a wide range of products, including digital wallets, payment gateways, and financial analytics tools. The company is committed to delivering high-performance, secure, and scalable solutions to its diverse clientele.

Issue

Slow and manual deployment processes, causing delays in software releases and increasing time-to-market.

Difficulty in managing infrastructure efficiently, leading to high operational costs and resource allocation issues.

The need for a scalable solution to handle increasing transaction volumes and user demands.

Ensuring security and compliance with industry standards for financial transactions.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Automated Deployment & Reliable Releases: Designed and implemented a DevOps pipeline, utilizing CI/CD for faster, more reliable releases, and efficient maintenance.

Infrastructure & Scalability: Used Infrastructure-as-Code (IaC) with AWS CloudFormation for efficient infrastructure management, and implemented containerization with Docker and Kubernetes for scalability and application portability.

Security Enhancements: Strengthened security with AWS Identity and Access Management (IAM) and encryption for data at rest and in transit.

Impact

Faster Deployment & Cost Efficiency

Accelerated deployment cycles from weeks to hours and significantly reduced operational costs through efficient infrastructure management and automation.

Scalability & Performance

Achieved high scalability to seamlessly handle increased transaction volumes.

Security & Compliance

Enhanced security and compliance with industry standards to protect sensitive financial data.

Scaling Data Analytics with Snowflake for a Leading MarTech Provider

Wed, 21 Aug 2024 12:39:16 +0000

Scaling Data Analytics with Snowflake for a Leading MarTech Provider

Optimize Data Scalability and Quality for Real-Time Marketing Insights with Snowflake

Company Overview

A leading company in the MarTech domain, this organization specializes in providing data-driven marketing solutions. Their platform leverages advanced analytics to deliver actionable insights that help businesses optimize their marketing strategies. Known for their innovative approach and commitment to quality, the company serves a diverse clientele across various industries.

Issue

The need to build a modern data analytics solution that can scale, ensuring security, compliance, and governance.

Integrating multiple data sources, including manufactured data, third-party data, and proprietary data.

Ensuring data is ingested, stored, and enriched efficiently while maintaining data quality and compliance.

Providing real-time insights and reporting using advanced visualization tools.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Comprehensive Data Platform: Built a robust data platform with AWS services like Amazon S3, AWS Glue, and Amazon Redshift, and implemented a Snowflake data warehouse for large-scale storage and processing.

Enhanced Data Quality & Reporting: Integrated third-party applications for data validation and used PowerBI for real-time insights and reporting, providing stakeholders with actionable data.

Effective Data Pipeline & Compliance: Developed a data pipeline for ingestion and enrichment, and ensured compliance with data privacy regulations through automated governance and consent management.

Impact

Scalable & Secure Data Platform

Built a data platform that is both scalable and secure, ensuring robust performance and protection.

Improved Data Quality & Compliance

Enhanced data quality and compliance through automated validation processes, providing reliable insights.

Real-Time Insights & Decision-Making

Enabled real-time reporting and insights, significantly improving decision-making capabilities and operational efficiency.

Facilitating DevOps and Infrastructure Modernization for Zykrr: A Seamless Transition from Azure to AWS

Tue, 20 Aug 2024 17:16:35 +0000

Facilitating DevOps and Infrastructure Modernization for Zykrr: A Seamless Transition from Azure to AWS

Seamless Azure to AWS Transition Enhancing DevOps and Infrastructure Modernization

Company Overview

Zykrr is a leading platform that provides real-time visibility, insights, and control over customer interactions across various channels such as SMS, WhatsApp, QR codes, email, kiosks, websites, and in-app communications. Their AI/ML-enabled platform delivers actionable insights and real-time visibility, enhancing customer and employee interactions. With a focus on automating critical customer experience management functions, Zykrr aims to deliver exceptional customer experiences and predict future actions based on sentiment analysis.

Issue

Migration & Modernization: Recreating the existing Azure setup on AWS while maintaining functionality and architecture.

Infrastructure Setup: Building and deploying a new application infrastructure on AWS, adhering to specified requirements and timelines.

Maintenance & Support: Reducing the efforts associated with deploying, maintaining, and supporting applications by leveraging AWS services.

Optimized Infrastructure: Achieving a robust, scalable, and secure AWS infrastructure.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Infrastructure Setup: Built Zykrr’s AWS infrastructure using S3 for scalable storage, EKS for managed Kubernetes, and RDS for PostgreSQL.

Application Deployment: Deployed React front-end on AWS CloudFront and S3, back-end microservices on EKS with Helm charts, and configured PostgreSQL and Redis on RDS and Elasticache.

Security & Performance: Secured infrastructure with network measures, API Gateway, and Application Load Balancer; ensured high availability and fault tolerance through multi-AZ deployments and CDN acceleration.

Impact

Successful Migration

Achieved a seamless migration from Azure to AWS with all functionalities intact.

Enhanced Deployment Efficiency

Streamlined deployment processes with AWS services, reducing operational overhead.

Enhanced Scalability, Reliability, & Cost Efficiency

Leveraged AWS’s scalability features for effective peak load management and high availability while implementing resource optimization strategies to achieve cost savings and efficient resource management.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Enhancing Sales Efficiency with an AI Sales Assistant through Advanced AWS Solutions

Tue, 20 Aug 2024 16:27:27 +0000

Enhancing Sales Efficiency with an AI Sales Assistant through Advanced AWS Solutions

From Scalability to Reliability: Enhancing Email Management and Deployment Efficiency

Company Overview

A leading company in the sales technology sector, this organization focuses on improving sales efficiency and productivity using its patented AI-based lead nurturing solution. The company’s AI Sales Assistant utilizes natural language processing (NLP) and machine learning (ML) models to ensure no lead is lost and to enhance overall sales performance. The AI Sales Assistant is designed to write, send, receive, and read emails, classifying them to take appropriate follow-up actions.

Issue

Need for Scalable infrastructure to manage a tenfold increase in daily email volume.

Requirement for Precise Email categorization and entity extraction to enhance data accuracy and processing.

Necessity to handle bounced and complaint email responses effectively to maintain communication integrity.

Urgency to Automate deployment processes to ensure continuous availability in QA and production environments.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Automated Lead Processing: Developed state machines using AWS Step Functions and Lambda for seamless lead processing and email response categorization.

Enhanced Email Validation: Implemented email validation using Debounce and Zero Bounce APIs, ensuring accuracy in the initial lead processing steps.

Integrated & Scalable Deployment: Configured AWS services for automated, zero-downtime deployments and integrated the solution with third-party CRMs like Hubspot and Pipedrive.

Impact

Scaled Email Operations

Expanded email sending capacity from 1,000 to 10,000 emails per day, meeting growing demands efficiently.

Enhanced Email Processing

Improved accuracy in processing and categorizing email responses, ensuring precise follow-up actions.

Achieved System Reliability

Implemented automated, zero-downtime deployments, ensuring high system reliability and availability.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Enhancing Customer Support with AI-Driven IVR Automation for a Leading InsureTech Provider

Tue, 20 Aug 2024 16:11:56 +0000

Enhancing Customer Support with AI-Driven IVR Automation for a Leading InsureTech Provider

Revolutionizing InsureTech Customer Service: Leveraging AI-Driven IVR for Seamless Support Automation

Company Overview

Issue

Resource-Intensive Integrations: Building and maintaining point-to-point integrations for each client was time-consuming and required significant resources.

Lengthy Integration Time: Integrating with third-party ERPs and payment systems took 8-12 weeks per client.

Limited Scalability: The existing process constrained the ability to serve multiple clients simultaneously.

Need for a Scalable Solution: A scalable, reusable integration framework was required to speed up the onboarding process for new clients.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

API-Based Integration Gateway: Implemented an API-based Integration Gateway using AWS services like Amazon API Gateway, AWS Lambda, and others for streamlined integration.

Solution Architecture & Deployment: Developed a four-component architecture (Flow, Filter, Transformer, Adopter) and built a deployment diagram to manage and transform inputs for various ERP systems, ensuring seamless integration.

Automated CI/CD & Ongoing Support: Established automated CI/CD pipelines for continuous integration and deployment, reducing manual intervention and errors, and provided ongoing support to maintain system robustness and scalability.

Impact

Enhanced Efficiency & Cost Savings

Automated IVR processes, significantly reducing manual intervention and operational costs while improving accuracy and efficiency in interactions.

Improved Customer Experience & Agent Productivity

Enabled faster, more reliable IVR task processing, boosting customer satisfaction and allowing agents to handle more complex interactions efficiently.

Comprehensive Compliance & Analysis

Provided detailed call records and summaries for review, ensuring regulatory compliance and supporting further analysis of IVR interactions

Enhancing Genomic Cancer Diagnosis Reports for a Leading Precision Medicine Company

Tue, 20 Aug 2024 16:05:48 +0000

Enhancing Genomic Cancer Diagnosis Reports for a Leading Precision Medicine Company

Faster decisions, better usability, efficient reporting, and increased engagement with genomic data

Company Overview

A leading company in the precision medicine domain, this organization specializes in genomic analysis and diagnostics to improve patient outcomes. Their platform leverages advanced technologies to provide actionable insights for personalized treatment plans, focusing on oncology, rare, and hereditary diseases.

Issue

The need to create user-friendly, detailed, and accurate genomic cancer diagnosis reports.

Ensuring the reports are easily interpretable by healthcare providers, aiding in swift and precise treatment decisions.

Integrating complex genomic data into a coherent and accessible format.

Maintaining high standards of data security and compliance.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Conducted User Research: Gained insights into the needs of healthcare providers and patients.

Developed Streamlined UX: Created a user-friendly design for genomic reports based on professional feedback and advanced data visualization.

Ensured Compliance: Adhered to industry standards for data security and regulatory compliance.

Impact

Enhanced User Satisfaction

Simplified genomic report interpretation for improved user experience.

Boosted Decision-Making

Enabled healthcare providers to make faster, more informed treatment decisions.

Increased Engagement

Fostered greater trust and engagement from healthcare providers and patients through streamlined reporting.

Driving Quality and Reliability for Modern Mobile and Web Applications: A Case Study for a Leading Online Consulting Platform

Tue, 20 Aug 2024 15:53:13 +0000

Driving Quality and Reliability for Modern Mobile and Web Applications: A Case Study for a Leading Online Consulting Platform

Developed a modern app with mobile and web support, integrated automated testing using Selenium, pyTest, and aiTest, and validated API quality with Postman for consistent, reliable results.

Company Overview

A leading start-up in the online consulting platform domain, this company focuses on connecting end-users with top industry professionals for expert consultation services. Known for its innovative approach and cutting-edge technology, the company aims to provide seamless and efficient solutions for both consultants and clients, ensuring high-quality interactions and problem-solving experiences. Applied AI Consulting created the modern stack with payment gateway integration for this customer, emphasizing quality engineering to ensure reliability and performance.

Issue

Building a modern application with mobile and web support for their consulting platform.

Ensuring application reliability and quality through comprehensive UI and API testing.

Integrating automation testing into the CI/CD pipeline to streamline releases and improve efficiency.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Agile Collaboration and Automation Framework Development: Collaborated closely with the development team in an agile environment to create test cases and architect a selenium-python-based UI automation framework and a pyTest-based API automation framework.

Advanced Testing with aiTest and CI/CD Integration: Utilized aiTest for executing automated tests across various devices and browsers, integrated with CI/CD pipelines to ensure consistent testing across QA, pre-prod, and prod environments, and validated API quality using Postman.

Real-Time Communication and Iteration Demos: Provided real-time updates on test execution and analytics via Slack and email, and conducted end-of-iteration demos to showcase automation progress and results.

Impact

Email Optimization

Modern App Development with Automation Integration: Successfully built a modern application supporting both mobile and web platforms, integrating an automation suite into the CI/CD pipeline for consistent and reliable testing.

Productivity Boost

Robust Testing Frameworks: Developed a selenium-python-based UI automation framework and a pyTest framework for API automation, ensuring comprehensive and dependable test coverage.

Scalable Infrastructure

Cross-Platform Testing and API Validation: Leveraged aiTest for automated testing across multiple devices and browsers, and integrated Postman for API quality validation and scalable testing.

Customer Testimonials

Applied AI Consulting has been instrumental in transforming our development and deployment processes. Their innovative approach to integrating automation into our CI/CD pipeline has allowed us to scale efficiently while maintaining top-tier application reliability. The cost savings and operational efficiency gains we've experienced have been profound, enabling us to focus more on strategic growth. Their ability to seamlessly blend technical expertise with a deep understanding of our business needs has made them an invaluable partner.
VP of Engineering

Boosting Healthcare Application Quality with Advanced QA Automation by Applied AI Consulting

Tue, 20 Aug 2024 15:40:35 +0000

Boosting Healthcare Application Quality with Advanced QA Automation

Company Overview

A leading health tech platform for the discovery, evidence generation, and clinical delivery for precision medicine. They help doctors and drug hunters make better decisions about providing the right therapy to the right patients at the right time. Over the past 10+ years, Velsera has developed industry-leading clinical software, knowledge, and perspectives on genetic diseases, including oncology, rare, and hereditary diseases. Their customer base includes some of the largest and most prestigious biopharma/biotech companies, government agencies, non-profits, clinical institutions, AMCs, diagnostic lab groups, and partnerships with OEM lab instrument providers and diagnostic assay developers.

Issue

Automation of over 1000 test cases across various sub-products, features, and modules to ensure efficient and consistent testing.

Need for a reliable and maintainable testing framework that could support the complexity and scale of the test cases.

Requirement of seamless integration with CI/CD pipeline, enabling automated testing during nightly builds.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Selected BDD Framework: Utilized Serenity for UI automation and Karate for API automation, leveraging BDD principles to enhance clarity and collaboration.

Successful Implementation: Automated the required test cases within the specified timelines, ensuring alignment with project goals.

CI/CD Integration: Integrated the automated framework with the CI/CD pipeline, enabling faster and more reliable software delivery.

Impact

Reduced Testing Cycle Time

Automation reduced testing cycle time by 50%, accelerating time-to-market for new features.

Improved Application Quality

Early detection of critical issues and increased test coverage led to fewer production issues and higher application quality.

Enhanced Customer Satisfaction

The integration of high-quality automation scripts into the CI/CD pipeline boosted customer satisfaction and retention.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Capital Market Platform Journey to Enhanced Scalability and Efficiency

Tue, 20 Aug 2024 15:27:41 +0000

Transforming Financial Services with AWS: Capital Market Platform Journey to Enhanced Scalability and Efficiency

Seamlessly Modernizing and Scaling a Capital Market Platform on AWS with Enhanced Monitoring and Collaboration

Company Overview

A leading financial services company focused on global emerging markets, this organization delivers up-to-date market insights from major financial centers to local investors. Known for its intelligence-driven approach, the company provides valuable information to help investors navigate diverse market conditions effectively. The company is committed to leveraging advanced technologies to enhance its services and maintain a competitive edge in the financial sector.

Issue

Migrating and modernizing the content management application to AWS.

Implementing AWS cloud operations and DevOps for continuous integrations and deployments.

Enhancing scalability and reliability of the applications.

Setting up efficient monitoring and operations dashboards.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Containerized and Scalable Deployment: Containerized the EMFI application using AWS ECS Fargate and set up an ALB for traffic distribution and auto-scaling.

Automated CI/CD and Infrastructure Management: Implemented CI/CD pipelines, automated environment deployments with ASG, and managed AWS services using Terraform.

Enhanced Monitoring and Migration: Established a Datadog dashboard for monitoring and facilitated migration from private GitLab to public GitHub.

Impact

Cloud Migration and Modernization

Successfully migrated the content management application to AWS, modernizing its infrastructure.

Scalability and Reliability

Achieved high scalability and reliability through containerization and automated deployments.

Improved Monitoring and Collaboration

Enhanced system visibility with Datadog and streamlined version control by migrating to GitHub.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Boosting Marketing Campaigns with AI: Enhancing Deployment Pipelines for a Leading AI Powered Recommendation Engine

Tue, 20 Aug 2024 15:23:08 +0000

Boosting Marketing Campaigns with AI: Enhancing Deployment Pipelines for a Leading AI Powered Recommendation Engine

Enhanced release speed, automated processes, scaled performance, and improved issue resolution.

Company Overview

A leading product in AIdriven marketing solutions, this product provides innovative tools to help marketers plan campaign content and generate marketing collateral based on campaign keywords. The company’s product, Marxeed, leverages Generative AI to deliver personalized recommendations and automate content creation, significantly enhancing marketing efficiency and effectiveness.

Issue

Ensuring the deployment of new features without disrupting ongoing operations.

Improving the maturity of deployment pipelines to support frequent and reliable releases.

Maintaining high performance and scalability to handle increasing user demand.

Automating the testing and deployment processes to reduce manual intervention and errors.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Implemented advanced CI/CD pipelinesusing Jenkins and AWS CodePipeline for automated build, test, and deployment processes, with automated testing frameworks like Selenium and JUnit to ensure high-quality releases.

Utilized AWS Elastic Beanstalkfor scalable application deployment and management, complemented by comprehensive monitoring and logging with AWS CloudWatch and the ELK Stack for real-time visibility and issue resolution.

Conducted regular training sessions for development and operations teams to streamline the deployment process and enforce best practices.

Impact

Increased Release Frequency

Boosted feature release rate by 46%, enabling faster delivery of new capabilities.

Enhanced Deployment Efficiency

Achieved 60% reduction in manual errors and deployment times through high automation.

Improved System Performance

Handled 35% more user load seamlessly and reduced issue resolution time by 40% with enhanced monitoring and logging.

Customer Testimonials

Partnering with Applied AI Consulting was a game-changer for us. Their expertise in automating our deployment pipelines and improving scalability has drastically reduced our time-to-market. The seamless integration of advanced CI/CD processes has not only enhanced our operational efficiency but also ensured that our platform can effortlessly handle increased user demand.
Sanju, Product Owner Marxeed

Transforming Financial Services with AWS: Capital Market Platform Journey to Enhanced Scalability and Efficiency

Tue, 20 Aug 2024 15:12:34 +0000

Transforming Financial Services with AWS: Capital Market Platform Journey to Enhanced Scalability and Efficiency

Capital Market Platform Journey to Enhanced Scalability and Efficiency

Company Overview

Issue

Migrating and modernizing the content management application to AWS.

Implementing AWS cloud operations and DevOps for continuous integrations and deployments.

Enhancing scalability and reliability of the applications.

Setting up efficient monitoring and operations dashboards.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Containerized and Scalable Deployment: Containerized the EMFI application using AWS ECS Fargate and set up an ALB for traffic distribution and auto-scaling.

Automated CI/CD and Infrastructure Management: Implemented CI/CD pipelines, automated environment deployments with ASG, and managed AWS services using Terraform.

Enhanced Monitoring and Migration: Established a Datadog dashboard for monitoring and facilitated migration from private GitLab to public GitHub.

Impact

Cloud Migration and Modernization

Successfully migrated the content management application to AWS, modernizing its infrastructure.

Scalability and Reliability

Achieved high scalability and reliability through containerization and automated deployments.

Improved Monitoring and Collaboration

Enhanced system visibility with Datadog and streamlined version control by migrating to GitHub.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

AI Based Sales Assistant that uses NLP and ML models

Mon, 12 Aug 2024 20:29:40 +0000

Enhancing Sales Automation with AI: A

Scalable Cloud-Native Application for a Leading AI Sales Assistant Provider

Cloud native app with scalable classification machine learning model

Company Overview

A leading company in the AI sales assistant domain, this organization specializes in leveraging artificial intelligence to automate and enhance sales processes. Their platform utilizes advanced machine learning and natural language processing to classify and respond to emails, process leads, and manage follow-ups, significantly improving sales efficiency and effectiveness. Known for their innovative solutions, the company serves a diverse clientele in the sales and marketing sectors.

Issue

Need for accurate classification of email content to streamline follow-ups and lead management.

Manual processes were time-consuming and prone to errors, impacting overall productivity.

Existing infrastructure lacked scalability to handle increasing data volumes and AI workloads.

Let’s discuss your use cases

Similar Use Cases

AAIC + Nisum Partner to Accelerate Enterprise Cloud Transformation with Agentic AI and AWS Innovation

A maturity model is a framework used to assess how well your organization uses a

Applied AI Consulting is proud to announce a strategic partnership with Nisum, aimed at delivering

Solution

Developed custom AI solutions leveraging machine learning (ML) and natural language processing (NLP) for accurate email classification and automated follow-ups.

Integrated the AI models seamlessly into the existing systems, ensuring minimal disruption and maximum efficiency.

Implemented a scalable AWS infrastructure to support high data volumes and complex AI operations.

Impact

Email Optimization

Significantly improved the accuracy and efficiency of email content classification and follow-ups.

Productivity Boost

Enhanced overall productivity, allowing the team to focus on higher-value tasks.

Scalable Infrastructure

Achieved seamless scalability, enabling the infrastructure to handle increased data volumes and AI workloads without performance issues.

Customer Testimonials

Applied AI Consulting’s expertise in building custom AI solutions with ML and NLP, along with their scalable AWS implementation, significantly enhanced our product’s performance. Their innovative approach and commitment to quality have driven tangible improvements, greatly benefiting our customers.
CTO 7Targets.ai

Top Logistics software company accelerates releases by direct feature branch deployment strategy

Wed, 12 Jun 2024 13:19:03 +0000

Streamlining Development with Our New Dev Deployment Strategy

In the fast-paced world of software development, efficient and reliable deployment processes are crucial. A Logistic software company faced challenges with their existing deployment strategy, particularly when it came to testing service dependencies before deploying to QA. To address these issues and enhance their workflow, we devised a new Dev Deployment Strategy that directly deploys feature branches to the Dev environment. Here’s a breakdown of why we made these changes, what they entail, and how they are implemented.

Why a Change Was Needed

Our client’s previous deployment process often led to failures when deploying directly to the QA environment. This was mainly because service dependencies were not thoroughly tested beforehand. The team wanted a way to test their code in a lower environment before merging it to QA, without the added complexity of merging into the dev branch first. The primary goal was to streamline the testing and deployment process, making it faster and more reliable.

What the New Strategy Involves

After extensive discussions, we established a Dev Deployment Strategy that allows feature branches to be deployed directly to the Dev environment. This approach offers several benefits:

Risk Reduction: It minimizes the risk of untested code being merged into the QA environment.
Efficiency: It speeds up the testing process by allowing developers and testers to work on feature branches without needing to merge them into a central dev branch.
Dependency Testing: It facilitates microservice dependency testing on feature branches, ensuring that any issues are caught early.
Simplified Merging: By eliminating the need to merge feature branch code into a specific branch for testing, we save valuable time and reduce complexity.

How We Implemented the New Strategy

To set up the Dev deployment workflow quickly, we leveraged GitHub Action reusable workflows. Here’s a step-by-step look at our implementation:

Java JAR Versioning: We automated the process of changing the Java JAR version from V2 to V1 , ensuring that both JARs remain separate.
Repository Integration: We integrated the new DEV deploy workflow into over 30 repositories.
Daily Refresh: Using cron jobs in GitHub Action, we refresh the Dev environment with the latest changes from QA every morning.

This setup ensures that the Dev environment is consistently up-to-date with the latest stable code from QA, while also allowing feature branches to be tested independently.

The Value of Our New Strategy

Implementing this new Dev Deployment Strategy has brought significant improvements:

Reduced Deployment Failures: By catching issues in the Dev environment before they reach QA, we’ve reduced the risk of deployment failures.
Faster Testing: The streamlined process speeds up testing, allowing developers and testers to work more efficiently.
Enhanced Dependency Testing: Feature branches can now be tested with their dependencies in a realistic environment, improving overall code quality.
Improved Release Process: By reducing the need for frequent merges between the dev and QA branches, we’ve made our release process smoother and more predictable.
Better Time Utilization: Developers can now test their code directly in the Dev environment, optimizing their time and efforts.

In summary, our new Dev Deployment Strategy has revolutionized our client’s development workflow. By directly deploying feature branches to the Dev environment, we’ve created a faster, more reliable, and more efficient process that benefits both their development and testing teams. This strategy not only enhances the ability to catch issues early but also ensures a smoother and more streamlined path from development to production.

Revolutionizing Conveyor Belt Manufacturing with Automated Defect Detection

Wed, 22 May 2024 11:56:34 +0000

The Product: Automating Defect Detection

Let’s dive into our game-changing product that automates defect detection in conveyor belt manufacturing. Instead of labor-intensive manual inspections, we’re using video recordings and image processing techniques for efficient defect detection.

Streamlining the Process

Manual inspections are time-consuming and labor-intensive. By automating this process, we streamline operations, maximize efficiency, and improve consistency in defect detection.

Why Automate?

Automating defect detection saves time, reduces costs, improves accuracy, and ensures scalability as production volumes increase.

How It Works

Our system captures video footage of conveyor belts, processes it using computer vision algorithms, and generates detailed defect reports, facilitating swift corrective action.

Challenges and Solutions

Defect Location Accuracy: Maintaining a constant speed of the conveyor belt and extracting frames at regular intervals improves defect location accuracy.
Thickness Variations: Advanced image processing techniques are needed to detect thickness variations accurately.
Resolution vs. Execution Time: Higher resolution improves accuracy but increases execution time; we balance these factors for optimal results.

Outcomes and Next Steps

The technology shows promising results, with significant improvements in defect detection accuracy and efficiency. As we move forward, our next steps include:

1. Refinement of Algorithms: We’re continually refining our algorithms to achieve even higher accuracy in defect detection across various types of defects. 2. Enhanced Reporting: We’re focusing on enhancing our reporting capabilities to provide more detailed insights into defect types, locations, and trends.

3. Integration with Manufacturing Systems: Our goal is to seamlessly integrate our defect detection system with existing manufacturing systems, ensuring a smooth and efficient workflow.

4. Continuous Learning and Improvement: We’re committed to a culture of continuous learning, exploring new techniques and technologies to further enhance our automated defect detection capabilities.

The Future of Manufacturing

With our innovative approach, we aim to revolutionize conveyor belt manufacturing, leading to improved productivity, reduced costs, enhanced product quality, and ultimately, increased customer satisfaction and competitiveness.

Applied AI Consulting

We’re a Pune-based IT service company specializing in AI, DevOps, and AWS Cloud Services. Our commitment to innovation and excellence drives us to create solutions that transform industries and pave the way for a smarter, more efficient world. Trust us to take your business to the next level with our expertise and forward-thinking strategies. Cheers to automation, efficiency, and a brighter future in manufacturing!

For more info, click

Navigating Response Generation: RAG vs. Fine-Tuning for Custom Knowledge Bases

Wed, 22 May 2024 11:28:54 +0000

Introduction

In the realm of AI-driven response generation from custom knowledge bases, two prominent approaches stand out: Retrieval-Augmented Generation (RAG) and Fine-Tuning. Both techniques offer unique advantages and challenges, making them crucial considerations for organizations delving into AI-driven content creation.

RAG: Retrieval-Augmented Generation

RAG represents a sophisticated approach that seamlessly integrates retrieval and generation techniques. It leverages a combination of pre-trained language models and information retrieval methods to provide responses. By retrieving relevant information from a knowledge base and then generating responses based on that context, RAG can offer a more nuanced understanding of queries and provide responses that are contextually relevant.

One of the key strengths of RAG is its versatility and scalability. Unlike traditional fine-tuning approaches that require task-specific training data, RAG can adapt to various domains and tasks without the need for extensive retraining. This makes it particularly appealing for applications where access to labeled data is limited or where the domain is constantly evolving.

However, RAG is not without its challenges. Some notable challenges include:

Occasional failure to provide accurate answers.
Longer response times due to reliance on retrieval-based methods.
Instances of “hallucinations” where the model generates incorrect or irrelevant responses.

Fine-Tuning: Precision Within Domains (RAG)

Fine-tuning, on the other hand, involves adapting pre-trained language models to specific tasks or domains by fine-tuning their parameters. This approach aims to optimize the model’s performance within a targeted domain, leading to high precision and accuracy for tasks within that domain.

One of the primary advantages of fine-tuning is its ability to achieve high precision within trained domains. By fine-tuning the model’s parameters and training it on domain-specific data, organizations can tailor the model to their specific needs and improve its performance for targeted tasks.

However, fine-tuning also has its limitations. Some key challenges include:

Requirement for annotated data for training, which can be time-consuming and resource-intensive.
Limited adaptability to new or unseen queries, as fine-tuned models may struggle with generalization.
Potential overfitting to the training data, leading to reduced performance on diverse queries.

Comparative Analysis: Unveiling Insights

In our comparative analysis, we delved deep into the performance of RAG and Fine-Tuning for response generation from custom knowledge bases. Through rigorous testing and evaluation, we gained valuable insights into the strengths and limitations of each approach.

Questions

RAG Response

FT(GPT3.5) Response

Comment

When was the information published for COHU, INC.’s Waste Reduction and Recycling Highlights program?

The date of publication for COHU, INC.’s Waste Reduction and Recycling Highlights program is unclear.

The information was published in 2018.

Rag fails to provide the answer.

Give me a list of investment types for COHU, INC.

COHU, INC. makes specific internal investments towards their Waste Reduction and Recycling Highlights program by implementing waste segregation processes, using energy-efficient machinery, and promoting a culture of recycling within their facilities.

There is no data available for the investment types of COHU, INC.

As there is some data present based on investment types but FT fails to respond.

What is the investment type for Forest conservation program in LOUISIANA-PACIFIC CORPORATION?

The investment type for Forest conservation program in LOUISIANA-PACIFIC CORPORATION is External Investment.

The investment type for the Forest conservation program in Louisiana-Pacific Corporation is Internal Investment.

Even data is not provided fro FT but still getting the answer but its not correct.

What are the investment types involved in DARDEN RESTAURANTS, INC.’s Retrofitting Restaurants program?

DARDEN RESTAURANTS, INC.’s Retrofitting restaurants program focuses on making their restaurants more energy efficient through various initiatives such as installing LED lighting and energy management systems. The types of investments involved include internal investments and infrastructure investments.

The investment type is Internal Investment.

Even data is not provided from FT but still getting the answer.

What is the investment type involved in CSG SYSTEMS INTERNATIONAL, INC.’s CSR program?

The investment types involved in CSG SYSTEMS INTERNATIONAL, INC.’s CSR program are External Investment and Donation/Fundraising.

The investment type is not specified for CSG SYSTEMS INTERNATIONAL, INC.’s CSR program as there is no data available.

No data is present for this company but still RAG providing the response.

What are the investment types involved in CTS CORPORATION’s CSR program?

The investment types involved in CTS CORPORATION’s CSR program are External Investment and Donation/Fundraising.

CTS CORPORATION’s CSR program involves Internal Investment.

In training data the investment type is present, hence FT is giving an answer. But in the actual doc, no data is present but still, RAG gives context.

What is the nonprofit name associated with THE J. M. SMUCKER COMPANY’s Education for Children program?

The nonprofit supported through THE J. M. SMUCKER COMPANY’s Education for Children program is the Boys & Girls Clubs of America.

The nonprofit name is Boys & Girls Clubs of America. The company partners with Boys & Girls Clubs of America to provide quality education programs for children.

Even though data is not provided from FT but still getting the answer but it’s not correct.

Our findings revealed that fine-tuning excelled in precision and accuracy, showcasing its capability to provide highly accurate responses within a specific context. However, we also encountered challenges with adaptability, as the fine-tuned model occasionally struggled with new or unforeseen queries, leading to inaccuracies and limited generalization.

On the other hand, RAG demonstrated a broader contextual understanding of queries, thanks to its integration of retrieval-based methods. This allowed RAG to provide responses with more nuanced context and relevance, especially for complex or ambiguous queries. However, we also noted occasional reliability issues, such as failures to provide accurate answers and instances of hallucinations.

Conclusion: Balancing Precision and Contextual Understanding

In our journey of comparing RAG and Fine-Tuning for response generation, we unearthed their distinct advantages and challenges. Fine-tuning excels in precision within a domain but grapples with adaptability, while RAG offers contextual understanding alongside occasional reliability concerns. Choosing between them hinges on organizational priorities and requirements, balancing accuracy and broader context.

Key Takeaways

RAG integrates retrieval and generation for contextual understanding and versatility.
Fine-tuning provides high precision within trained domains but may struggle with adaptability.
The choice depends on specific organizational needs, available resources, and priorities.

Final Thoughts

As organizations navigate the landscape of AI-driven content creation, understanding the nuances of RAG and Fine-Tuning is paramount. By leveraging their strengths and mitigating challenges, organizations can harness the power of AI for effective response generation from custom knowledge bases, ultimately enhancing customer experience and operational efficiency.

Revolutionizing Enterprise Search with MyGPT: A Comprehensive Overview

Wed, 22 May 2024 10:36:21 +0000

*Note: We are using MyGPT instead of actual product name to hide the identity of the customer for whom we created this product. *

In today’s data-driven business landscape, the ability to access and extract insights from vast amounts of information is crucial for success. Traditional keyword-based search tools are becoming obsolete as enterprises demand more sophisticated solutions capable of understanding context, intent, and meaning. Enter MyGPT, an Enterprise Grade Search assistant powered by cutting-edge AI technologies, designed to address the challenges and complexities of modern enterprise search.

Problem Statement

The evolution of search technology, particularly with Generative AI and Large Language Models (LLMs), has introduced new possibilities like semantic search and query retrieval. However, existing open-source solutions pose several challenges:

Manual Efforts for Data Loading/Training: Tedious manual processes are required for data loading and training in current solutions.
Public LLM Data Privacy Issue: Utilizing public LLMs may compromise data privacy and security.
UI Interaction Complexity: User interface interactions are often overly complex or poorly structured.
Lack of Content Access Control: Limited control over permissions for accessing content.
No History of Auditing and Logging: Absence of comprehensive auditing and logging mechanisms for data.

The MyGPT Solution

MyGPT is a game-changer in the realm of enterprise search, it is an Enterprise Grade Search assistant designed to cater to the needs of enterprise users. MyGPT engages in conversation with users and provides responses sourced from the enterprise’s own document and knowledge base. Leveraging the RAG approach, MyGPT stores data in vector format and conducts semantic search during response generation. Additionally, MyGPT incorporates permission checks to ensure that users only access content they are authorized to view. This approach offers a faster and more cost-effective alternative to traditional fine-tuning methods.

offering an array of features and functionalities tailored to meet the demands of enterprise users:

1. Crawler for Data Discovery

MyGPT’s Crawler revolutionizes the way businesses discover and access data within their network drives.
It leverages asynchronous multiprocessing and a sophisticated producer-worker code design.
Automatically discovers and indexes relevant data sources from configured network drives such as SharePoint and OneDrive.
Ensures real-time and accurate document discoverability.
Offers seamless onboarding for clients by automatically configuring during the signup process.
Scheduling capabilities allow organizations to define time-based discovery processes.

The insights and analytics provided by MyGPT’s Crawler offer valuable visibility into the file discovery process, empowering businesses with actionable intelligence to optimize data management strategies, identify trends, and make informed decisions regarding their information assets.

MyGPT offers a cost-effective solution with Multi-Tenant Sign-In, enabling multiple clients to share resources on the MyGPT platform.
Streamlines onboarding and reduces infrastructure costs.
Promotes quicker client integration through SAAS signup processes.
Maintains data security and access control through RBAC mechanisms.

B. Single-Tenant Deployment

Provides clients with dedicated resources and infrastructure control.
Ideal for organizations prioritizing data isolation, enhanced customization, and stringent compliance requirements.
Enables tailored MyGPT configurations to specific requirements.

3. Private Embedder for Data Security - Utilizes private cloud networks for secure text-to-vector conversions with configurable parameters for optimization.

4. LLM for Question Answering - Hosted within private cloud networks, MyGPT’s LLM feature allows for controlled response generation with modularity and cost-effective deployment options.

5. Role-Based Access Control (RBAC) - Replicates existing permission trees to ensure compliance, protect sensitive data, and streamline access management.

6. Communication Channels - Seamless integration with Microsoft Teams Bot and Webchat UI enables easy interaction and context maintenance.

7. Kubernetes Deployment for Scalability - Cloud-native architecture ensures efficient scalability, cost-effectiveness, and easy maintenance through DevOps pipelines.

Business Values and Impact

Efficiency: MyGPT automates tedious tasks, reducing manual efforts and increasing productivity.
Security: Private cloud hosting and RBAC ensure data privacy and compliance with regulations. Cost-Effectiveness: Cloud-native deployment and scalable infrastructure optimize resource utilization and reduce operational costs.
Customizability: Configurable parameters and deployment options allow for tailoring MyGPT to specific enterprise requirements.
User Experience: Streamlined UI/UX and seamless integration with communication channels enhance user satisfaction and engagement

Conclusion MyGPT represents a paradigm shift in enterprise search, leveraging AI advancements to deliver a comprehensive, secure, and efficient solution. With its range of features addressing data privacy, access control, automation, and scalability, MyGPT empowers businesses to unlock the full potential of their data assets and drive informed decision-making.

How AAIC Enabled a Global Financial Services Company Leverage DevOps to Deliver Financial Market Intelligence in Near-Real Time

Wed, 21 Feb 2024 11:12:36 +0000

Executive Summary

EMFI Group is a global financial services company specializing in delivering intelligence-driven services with a focus on emerging markets. EMFI provides up-to-date market information from major financial centers of the world to local investors.

Challenges

Market information and business intelligence are by their nature ephemeral and require constant updates to be useful.

Although EMFI was already on AWS cloud, the organization was in urgent need of modernization of their Content Management Application, so as to cut the time needed to build & deploy new application infrastructure by more than 99 percent.

EMFI partnered with Applied AI Consulting (AAIC) to leverage AWS Services in deploying, maintaining, and supporting their existing applications and use the Devops building blocks to implement a robust Cloud-based automation framework with a view to enabling DevOps on their platforms.

About AAIC

Applied AI Consulting (AAIC) is a bespoke AI and Cloud Consulting Service. The core team comprises industry veterans with 25+ years of cumulative experience in building, architecting, and delivering cutting-edge technology solutions focused on resolving customer pain points.

Partner Solution

AAIC’s engagement focused on EMFI’s Cloud operations, with a view to enabling DevOps for EMFI workloads and CIs in their AWS cloud.

AAIC engineers team worked with EMFI technical leadership and product owner team to develop modernized business solutions, in the following ways:

Containerizing using AWS ECS Fargate service. Fargate’s serverless nature meant quicker deployment. In addtion to being a serverless compute engine for containers, Fargate works with both Amazon ECS and Amazon EKS.

The use of AWS Fargate means EMFI doesn’t need to provision or manage servers, as AWS Fargate manages server infrastructure. This allows EMFI to double down on innovating and developing its applications. And of course, to pay only for the resources needed to run their containers.

Building CI/CD pipeline for EMFI application using AWS Developer tools such as AWS CodeBuild, AWS CodePipeline, AWS CodeDeploy and Amazon CloudWatch services. Automated building and deployment of application code to the ECS cluster every time code changes meant a signiﬁcant saving in time and effort.
Setting up AWS Automatic Load Balancer (ALB) which distributes the incoming traffic across multiple targets, such as EC2 instances, containers, and IP addresses, which automatically scales workloads.
Setting up Operations/Monitoring dashboard with the help of AWS Cloudwatch. The dashboard includes details about ECS services like CPU/Memory utilization, Disk Space utilization, Network Insights, ECS service task status, and ECS service deployment status. This gave developers an instant and real-time view of their infrastructure, allowing them to spot and stop potential issues before they occurred.
Help in migrating private GitLab to public SaaS GitHub. Git, an open-source version-control system saves snapshots of a software project, allowing users to maintain a record of all changes. Unlike conventional version control, Git allows users to work on different versions, manage the simultaneously and test them by merging the ideal variant seamlessly. This freedom to work on multiple versions simultaneously is an invaluable asset for DevOps teams. Using GitHub allows EMFI to leverage the large and growing GitHub community and its contributions by way of functionality that might otherwise be missing.
Automation of the application scaling, with the implementation of Cloudwatch alarms and app auto-scaling policies. These policies involve target tracking policies and scheduled-based policies that will increase or decrease task count in ECS service based on the specified criteria or schedule to handle the workload.
Creating application infrastructure with AWS services using Terraform. (Terraform’s familiarity with EMFI developers, made it the IaC tool of choice) This includes building a scalable and secure network layer (using AWS VPC, AWS WAF, NACL, VPC Endpoints, etc.), database layer (using AWS RDS) and application layer with logging and monitoring enabled (using AWS ECS, AWS Cloudwatch, etc.)

Architecture

Architecture defines a high-level model of solution and describes how the resources are interlinked with each other. Within AWS the architecture utilized load balancers to distribute incoming traffic across ECS Fargate containers which provided stability across the application

Deployed AWS Services

AWS ServiceUsage of service ECSAmazon Elastic Container Service (Amazon ECS) is a highly scalable, fastcontainer management service that makes it easy to run, stop, and manage containers on a cluster.ECS ClusterAn Amazon ECS cluster is a regional grouping of one or more container instances on which you can run task requests.ECS Task DefinitionTask definition is basically the blueprint describing the docker containers that are used to run the application.ECS ServiceService defines the minimum and the maximum tasks that are running from a single task definition at any given point.EC2 InstanceEC2 stands for Elastic Compute Cloud. It is nothing but a basic computer virtually present in AWS cloud which you can access and do whatever you can do on a basic computer.VPCA virtual private cloud (VPC) is a secure, isolated private cloud hosted within a public cloud.ALBThe Application Load Balancer is a feature of Elastic Load Balancing that allows a developer to configure and route incoming end-user traffic to applications based in the AWS public cloud.IAMAWS Identity and Access Management (IAM) enables you to manage access to AWS services and resources securely.RDSAmazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud.

Results and Benefits

Being on AWS opens up an array of new services and technologies that are now significantly more accessible to the average IT group
AWS opens up new possibilities to integrate, including best of breed tools & services to connect to other systems
Use of Terraform provides create, change, and version infrastructure safely and efficiently.
Elasticity, load balancing and the capability to scale automatically is a big bonus which added robustness in the system and increased capability to handle the 3x load.

About the Partner

AAIC is a digital technology services company providing Digital Engineering, Cloud Engineering services, and AI-powered Digital Marketing services. We design & build digital platforms and products with Cloud and Opensource at the focal point by working on cutting-edge technologies.

Site: 91SpringBoard, Sky Loft, Creativity Mall,

Opposite Golf Course, Yerawada, Pune, India 411006

www.appliedaiconsulting.com

Meeting 7Targets’ Automation Targets

Tue, 30 Jan 2024 09:36:00 +0000

7Targets

7Targets is a SaaS B2B company in the Sales and Marketing domain focused on lead nurturing. The company’s flagship product ‘AI Sales Assistant’ provides users with the capability to automate emails creation and conduct follow-ups of received leads. It is also able to read received responses and act accordingly.*

Customer challenge

Despite offering an AI driven solution (for lead development) and SaaS product, 7Targets team was performing manual deployments in their environments. This made the development process highly susceptible to human errors, reduced efficiency, and affected the code quality.

7Targets team was looking was faster, efficient upgrade and rollback process in the application deployments.

Specific project-level challenges were:

Increased development and deployment time with increased backlog.
Increased release cycles for production upgrades
Process did not allow optimal utilization of resources

Solution

To address the specific challenges faced by the client, Applied AI Consulting designed the following solution:

Implemented GitLab for versioning control. Git is a popular open source code repository as well as a software development platform for collaborative working on DevOps projects. Not only is the platform free (although there is a paid version, too.), unlike GitHub, it offers CI/CD capabilities, and a location for online code storage and issue tracking.
GitLab repository allows hosting of different versions, enabling users to inspect code and roll back to a desired version in the event of any problems. GitLab supports each stage of the software development lifecycle, enabling DevOps capabilities. It comes with a continuous integration (CI) Capabilities built in, which allows development teams to automate code building and testing. GitLab also provides security features, presenting developers with scans—within their native CI pipeline—and dashboard assists showing vulnerabilities. (CI/CD stands for Continuous Integration-Continuous Delivery/Deployment. It is a set of practices that allows developers to introduce automation into software development stages—building, testing, deployment, etc.—so they can make changes to the code and deploy them in production without waiting for a release window).
GitLab flow model was implemented for branching strategy.
GitLab is built on the same basis as GitHub, and offers similar functionality for source code management but has the advantage of being free for individuals and small businesses, compared to GitHub which charges for private repositories. For these reasons, and because the client was familiar with GitLab, AAIC chose to implement GitLab as the version control system. Gitlab CIs were set up to be triggered whenever any commit was made to the corresponding branch of each repository. Viz. As soon as the code was merged into develop and main branch, Gitlab CI would get triggered and pull the code from repository and trigger the build the code.
Based on the repository storage type, GitLab CI generates Zip files and container images with the libraries and tools needed to upload those to the selected AWS S3 bucket and AWS Elastic Container Registry (ECR) respectively for respective development and production environment.
Upon successful build, Another Gitlab CI job will be triggered and it will deploy the artifacts (Containers and Lambda Zip files ) to the Lambda and ECS environments.
At the end, series of automated tests are executed to verify deployments and, in the event of any glitches, Gitlab CI rollbacks implemented to deploy the previously stable running version of the application.

Tech Stack

Several AWS services used as part of the solution, including

AWS S3, AWS ECR, AWS Lambda, AWS ECS Fargate,

Go Live Date: December 2023

Outcome(s)/results

With fully automated CI/CD solution, any possibility of human prone errors was eliminated, which speeded up the entire development cycle
With automation in the development cycle, the time to market of this SaaS application was reduced to 80% with three to five upgrades on daily basis
As the development lifecycle was automated, developers can now spend more time in development rather than in operations.

Architecture diagrams of the speciﬁc customer deployment

About AAIC

We are automation experts, with a majority (>60%) of our workforce AWS-certified. We assist you in applying intelligence to the Cloud and DevOps, as our name suggests. Our AWS certified experts create high-performing cloud apps by utilizing intelligence components and smart integrations to accelerate your digital transformation journey.

ML Testing: Ensuring Accuracy and Reliability in Machine Learning Systems

Mon, 05 Jun 2023 09:34:54 +0000

As the demand for machine learning (ML) systems continues to rise, the importance of testing and quality assurance cannot be overstated. ML models are only as good as the data they are trained on and the algorithms that power them. Without proper testing, these models can produce inaccurate or unreliable results, leading to serious consequences in industries such as healthcare, finance, and transportation.

At Applied AI Consulting, we understand the critical role that testing plays in ensuring the success of ML systems. As a leading digital engineering company that specializes in AI solution consulting and implementation, we have developed a comprehensive approach to ML testing that covers pre-train, post-train, and production stages.

Try aiTest for testing any application UI or API or creating automation suite on the fly.

In this blog, we will summarize our ML testing framework and share some best practices for ensuring accuracy and reliability in ML systems.

Level 1: Smoke Testing

Smoke testing is a quick and simple way to ensure that an ML model is functioning as expected. It involves running a few basic tests on the model to check if it can produce the desired output. These tests can include checking the model’s accuracy on a small dataset, verifying that it can handle missing values or outliers, and ensuring that it can handle different input formats.

Level 2: Integration Testing and Unit Testing

Integration testing involves testing the interaction between different components of an ML system. This can include testing how data is passed between different modules, how models are trained and evaluated, and how results are generated. Unit testing, on the other hand, focuses on testing individual components of the system, such as algorithms or data processing pipelines. Both integration and unit testing are essential for identifying and fixing bugs and ensuring that the system is functioning as a whole.

🔢 Data

Testing ML systems also requires careful attention to data. It is important to ensure that the data used to train and test the model is representative of the real-world data that the model will encounter. This can involve data cleaning, data augmentation, and data validation. It is also important to consider the ethical implications of the data used, such as bias and privacy concerns.

🤖 Models

When it comes to testing ML models, there are several approaches that can be used. One common approach is to use test datasets that are separate from the training data. These datasets should be representative of the real-world data that the model will encounter and should be used to evaluate the model’s accuracy, precision, recall, and other performance metrics. Other approaches include stress testing, where the model is tested under extreme conditions, and adversarial testing, where the model is tested against intentionally crafted inputs designed to deceive it.

Post-train tests

Post-train tests are used to ensure that an ML model is still performing as expected after it has been deployed. These tests can include monitoring the model’s performance over time, testing how it handles new data, and verifying that it is still accurate and reliable. It is important to have a robust monitoring system in place to catch any issues that may arise and to ensure that the model is always performing at its best.

Production

Once an ML model has passed all the necessary tests, it is ready for production. However, testing does not stop here. It is important to continue monitoring the model’s performance in production and to have a plan in place for handling any issues that may arise. This can include automatic failover systems, backup models, and human oversight.

ML model testing : performance metrics and evaluation method

Machine learning model testing is essential to ensure the model is performing as expected and can generalize to unseen data. Performance metrics such as accuracy, precision, recall, and F1 score can be used to evaluate the model. The evaluation method will depend on the specific machine learning task.

Ready to explore AI ML testing strategy or want to hire expert AI ML testers, contact us.

Conclusion

Testing is an essential part of developing and deploying ML systems. By following best practices and using a comprehensive testing framework, we can ensure that our models are accurate, reliable, and safe for use in real-world applications. At our company, we are committed to providing the best digital solutions for AI ML use cases and GPT/LLM Apps, and we believe that proper testing is a key part of achieving that goal.

API Automation: Best Practices for Testing and Development

Fri, 26 May 2023 07:46:04 +0000

As a Cloud Native Development and AWS DevOps Managed Service Provider, Applied AI Consulting is committed to providing end-to-end cloud solutions and services. One of the key areas we focus on is API automation, which allows businesses to streamline their testing and development processes and improve overall efficiency. In this blog, we will explore the best practices for API automation and testing, and provide practical tips for getting started.

Phase 1: Getting everything ready

Before you can start automating your API tests, you need to make sure you have everything you need. This includes setting up your testing environment, creating a collection of API requests, and familiarizing yourself with the API documentation. You should also have a clear understanding of the API’s functionality and the expected response for each request.

Setting up Postman

Postman is a powerful tool for API testing and automation. To get started, you will need to download and install the Postman app on your computer. Once installed, you can create a new collection by clicking the “New” button in the top left corner of the Postman window.

Listing/New Request

To get started, create a new request in your collection. This can be done by clicking the “New” button in the top left corner of your Postman window. From here, you can enter the URL for the API you want to test, along with any required headers or parameters.

Phase 2: Automated Tests Development

Once you have your collection of API requests, you can start developing your automated tests. This involves writing code that will run through each request in your collection and verify that the response is correct.

Random Data Generation

To make your tests more robust, it’s a good idea to use random data generation. This means that each time you run your tests, they will use different data to ensure that all possible scenarios are covered.

Mocking APIs

Another useful technique for API testing is mocking. This involves creating a mock API that mimics the behavior of the actual API, allowing you to test your code in a controlled environment.

Phase 3: Automated Tests Execution

Once you have developed your automated tests, you can start executing them. This involves running your tests against the API and verifying that the response is correct. You should also monitor the performance of your tests and make any necessary adjustments to ensure that they are running efficiently.

Verify ‘User is Deleted’ Request

One important test to include is the “User is Deleted” request. This involves sending a request to delete a user from the API and verifying that the user has been successfully deleted.

Conclusion

API automation and testing are essential for businesses that want to streamline their development processes and improve overall efficiency. By following the best practices outlined in this blog, you can ensure that your API tests are robust, efficient, and effective. Whether you are a small startup or a large enterprise, API automation can help you achieve your goals and stay ahead of the competition.

Driving Quality and Reliability for modern mobile and web applications

Tue, 23 May 2023 12:03:02 +0000

Challenge & Test Requirements

Build a modern application with mobile and web support for a ConsultTech customer
Ensure application reliability and quality through UI and API testing
Integrate automation testing into the CI/CD pipeline

Solution and Approach

Worked in agile mode alongside the development team to create test cases and architect an automation framework
Created a selenium-python based framework for UI automation and pyTest framework for API automation
Utilized aiTest for automation test execution across multiple devices, browsers, and their versions
Used aiTest to execute the API Postman collection for API quality validation and scale testing

ROI

Integration with the CI/CD pipeline ensured consistent testing on every build push to QA, pre-prod, and prod environments
The customer receives updates on test execution status, success/failure status, and other analytics via Slack and email
End of iteration demos showcased the automation and run results

Results

Successfully built a modern application with mobile and web support
Automation suite integrated into the CI/CD pipeline for consistent testing
Selenium-python-based framework for UI automation and pyTest framework for API automation ensured reliable testing
aiTest enabled automation testing across multiple devices, browsers, and their versions
Integration with Postman collection and execution helped to validate API quality and scale testing

Accelerating QA and Ensuring Performance: A Comprehensive Test Automation Case Study

Tue, 23 May 2023 11:52:15 +0000

Challenge & Test Requirements

Create a TestNG Maven framework for UI automation and RestAssured for API automation
Do functional UI and API automation and performance testing (load testing) to support 25,000 concurrent users on API calls and 5,000 concurrent users on UI with 30 million plus record in the database
Integrate automation with CI CD pipeline

Solution and Approach

Used AAIC’s accelerator and product aiTest to execute multi-browser tests for UI product as well as load test for the backend and frontend application
Created a TestNG Maven framework with Selenium & Java for UI automation and RestAssured for API automation
Used aiTest for UI load testing and Jmeter with aiTest for API load testing
Implemented parallel run to reduce build qualification test time from hours to 9 minutes for UI tests and 2-3 minutes for API run

ROI

First batch of UI and backend products went live in early April with no production issues reported
With a good amount of automation, the customer can roll out updates and new features every 2 weeks

Results

AAIC’s 5 member QA team, led by a QA lead, successfully completed all three core responsibilities of the project: deciding, architecting, and implementing the right framework, doing functional UI and API automation, and performing load testing for 25,000 concurrent users on API calls and 5,000 concurrent users on UI with 30 million plus record in the database
Automation integrated with CI CD pipeline, enabling fast and efficient build qualification tests
Successful test for 25,000 concurrent users for backend APIs and 1000s of concurrent users for UI application, providing confidence in the application’s performance and scalability

Enhancing Healthcare Application Quality with Applied AI Consulting’s QA Automation Service

Tue, 23 May 2023 11:37:50 +0000

Challenge & Test Requirements

A healthcare technology company needed to automate over 1000 test cases across different sub-products, features, and modules.
The customer was also looking for a reliable and maintainable framework that could be integrated with their CI CD pipeline for nightly builds.

Solution and Approach

Applied AI Consulting provided the healthcare technology company with a team of 7 QA Engineers, led by a Sr. QA Architect who was responsible for architecting, designing, and implementing the right automation framework.
The team chose a BDD-based framework and used Serenity for UI automation and Karate for API automation.
The team implemented the framework and automated the required test cases within the desired timelines.

ROI

The Applied AI Consulting team’s automation efforts enabled the customer to reduce their testing cycle time by 50%, resulting in faster time-to-market for new features and enhancements.
Fewer production issues resulted in reduced downtime and improved overall efficiency.
The customer was able to meet their testing requirements and achieve a higher degree of test coverage, resulting in increased customer satisfaction and retention.

Results

The automation scripts enabled the team to detect critical issues earlier in the development cycle, reducing the cost of defects and improving overall application quality.
The Applied AI Consulting team was able to deliver high-quality automation scripts that were integrated into the CI CD pipeline, providing confidence on the build quality.
Overall, the Applied AI Consulting team’s expertise in QA Automation helped the customer achieve their testing requirements, resulting in better quality, faster time-to-market, and increased user satisfaction.

AITest: Streamline Your Performance Testing and Cross-Browser Testing with AI

Thu, 23 Feb 2023 08:04:00 +0000

Introduction

Performance testing and cross-browser testing are crucial for ensuring that your application is reliable and performs well for all users, regardless of their device or browser. However, these types of testing can be time-consuming and resource-intensive. In this blog, we’ll share how AITest can help you streamline these processes and achieve significant value realization for your organization.

How aiTest was born?

aiTest was born out of a real-life problem faced by a payment gateway company in Pune, India. The company was handling thousands of transactions per day, and one day, without any changes to the code, the app suddenly started failing. More than 5-8% of transactions failed daily, resulting in a loss of thousands of dollars per hour.

The dev and QA teams were unable to identify the cause of the problem, and that’s when they called on our team for help. We had our own deployment automation solution. We tweaked that to create aiTest. aiTest simulated 50-70 different browsers, each with a different version, and ran a selenium test simulating an e-commerce transaction. This revealed that the payment gateway issue was caused by Chrome v40, which had been released by google with a security fix. The fix required developers to make adjustments to their code. Google had given a 1-year advance notice on this one. However, the payment gateway company had not made this change, and as a result, all payments were failing on Chrome v40.

Many user mobiles had auto-updated their browser versions. Hence, even though there was no code change from the development teams, the app has stopped working because the environment had changed.

This experience led to the creation of aiTest, which helped many other clients after that. Simulating users via browsers on the cloud was different than using tools like JMeter. aiTest’s goal was to make it easier for businesses to get a REAL LIFE ENVIRONMENT, although a simulated one. This helps identify and solve problems like the one faced by the payment gateway company, ensuring the smooth functioning of their apps and minimizing the risk of financial losses.

What are Shift-Left Testing and Cross-Browser Testing?

Shift-left testing is an approach where testing is done closer to the development phase, rather than later in the release cycle. The idea is that if defects are found early in the development process, they will be easier and less costly to fix. Research has shown that fixing a defect during the early development phase can cost as little as $25 while fixing it after release can cost up to $7,600.

Cross-browser testing, on the other hand, involves testing an application on multiple browsers to ensure that it works correctly for all users. According to Global Stats, the top six most used browsers are Chrome, Firefox, Safari, Opera, Samsung, and Microsoft Edge. It’s important to test on these browsers, as well as any others that may be relevant for your users, to ensure that your application

There are several challenges that people face when releasing apps due to inadequate cross-browser testing. These include:

Compatibility issues: Apps may not function properly or display correctly on different browser versions or platforms, leading to user frustration and abandonment of the app.
Performance issues: Apps may experience slow loading times or other performance issues on certain browsers or devices, leading to a poor user experience.
Security vulnerabilities: Apps may be vulnerable to security threats or exploits on certain browsers, which can compromise user data and damage the reputation of the app.
User experience: Without cross-browser testing, apps may not provide a seamless and consistent user experience across different browsers and devices, leading to a lack of user satisfaction.
Development time and cost: Fixing issues that arise due to inadequate cross-browser testing can be time-consuming and costly, leading to delays in the release of the app and higher development costs.

The best way to use aiTest is to integrate it into your CI/CD pipelines.

How to integrate AITest into CI/CD pipeline?

Integrating AITest into your CI/CD pipeline allows you to automate the testing process, ensuring that your code is always tested before being deployed. There are a few steps you can follow to integrate AITest into your pipeline:

Get your AITest API key: You will need to get an API key from AITest to use their services in your pipeline. You can do this by creating an account with AITest and generating a new API key from the account settings.
Set up your pipeline: Depending on the tools you are using for your CI/CD pipeline (such as Jenkins or Azure DevOps), you will need to set up a new job or stage in your pipeline to run your tests with AITest.
Install the AITest plugin: If you are using Jenkins, you will need to install the AITest Jenkins plugin to allow Jenkins to communicate with the AITest API. If you are using a different tool, you may need to install a different integration.
Configure your pipeline to run AITest: Once you have the AITest plugin or integration set up, you can configure your pipeline to run AITest as part of the testing process. This may involve specifying the API key, selecting the tests you want to run, and specifying any other options or parameters.

By following these steps, you can easily integrate AITest into your CI/CD pipeline and automate the testing process for your code.

Why aiTest is used widely in Fintech?

Fintech companies are releasing more mobile apps nowadays. Hence it’s important for them to quickly and easily test their applications for performance and cross-browser compatibility. By integrating AITest into their CI/CD pipeline, fintech companies and mobile app developers can ensure that their products are ready for release and will function properly on a wide range of devices and browsers. This can help to reduce the risk of defects and improve the overall user experience, leading to higher customer satisfaction and retention. Additionally, AITest can help fintech companies and mobile app developers to optimize the performance of their products, ensuring that they operate smoothly and efficiently even under heavy load.

What if you don’t have automated tests?

This can be a problem. If you don’t have any automated tests, you are clearly spending a huge amount of money on testing, and not getting the required covered. Or speed.

Hire an automated test developer. This can help in getting at least 20% of core tests covered for a wide range of devices at high speed. Each application has that core 20% area which is key to its success. At a minimum, that 20% portion will protect quality for 80% of your key customers.

Using ChatGPT to accelerate test case generation

chatGPT and aiTest form a golden combination. Create tests faster-using chatGPT and put them into aiTest to make sure every release henceforth covers those tests. We have released a few videos on how to use chatGPT for generated selenium tests as well as unit tests.

What’s Next

World is moving faster with ai tools. There is no point in getting left behind. Use good tools effectively, quickly, and fail fast to provide feedback loops into these tools. So that your journey to quality becomes smooth as early as possible.

The Death of Freelance Content Writers: How AI is Changing the Way People Work

Thu, 23 Feb 2023 07:27:00 +0000

The digital age has changed the way people work, and the freelance content writing industry is no exception. With the rise of artificial intelligence (AI) and automated content generation, many content writers are feeling the pinch. In this blog post, we’ll explore how AI is changing the way people work and why freelance content writers may soon become a thing of the past.

When it comes to content writing, AI is quickly becoming the preferred choice for many businesses. AI-powered content generation tools can generate high-quality content at a fraction of the time and cost of hiring a freelance writer. With AI-generated content, businesses can quickly produce content for their website, blog, or social media channels.

AI-generated content can also be tailored to each customer’s needs. AI-powered content generators can analyze customer data and create content that is tailored to their specific needs. This means that businesses can create content that is more relevant and engaging to their target audience.

AI-generated content can also be more efficient and cost-effective than hiring a freelance writer. With AI-generated content, businesses can save time and money by eliminating the need to hire a freelance writer. AI-generated content can also be more accurate and consistent than content written by a freelance writer.

However, there are some drawbacks to AI-generated content. For example, AI-generated content can lack the personal touch that comes with content written by a human. Additionally, AI-generated content can be difficult to customize, meaning that businesses may not be able to customize their content to meet their specific needs.

Despite the drawbacks, AI-generated content is quickly becoming the preferred choice for many businesses. As AI technology continues to evolve, it’s likely that AI-generated content will become more sophisticated and personalized. This could mean that businesses will be able to create content that is highly targeted and tailored to their customers’ needs.

While AI-generated content may be the future of content writing, this doesn’t mean that freelance content writers are doomed. Freelance content writers can still find success in the digital age by embracing the changing landscape. Freelance content writers can use AI-generated content as a starting point and then customize it to meet their clients’ needs. Additionally, freelance content writers can use their expertise to create content that is more personalized and engaging.

In the end, the death of freelance content writers may be inevitable, but it doesn’t have to be the end of their careers. By embracing the changing landscape and leveraging the power of AI, freelance content writers can still find success in the digital age.

Here are a few suggestions on Social Media Promotion of this blog: Are you a freelance content writer looking for ways to stay relevant in the digital age? Check out these tips on how to use AI to generate high-quality content and stay ahead of the competition.

The death of freelance content writers is upon us! Find out how AI is changing the way people work and how freelance content writers can still find success in the digital age. #freelancecontentwriters #AI #contentwriting

Ten Most Common Cloud Migration Challenges (and how to resolve them)

Thu, 23 Feb 2023 06:50:00 +0000

There is no doubt that cloud computing is one of the biggest technological disruptions in modern times, and it is only going to grow. With an estimated CAGR of 18%, it looks set to hit USD 623 billion by 2023. But despite these high growth forecasts, there are many businesses that have yet to embrace the cloud. The biggest factor in their hesitation is uncertainty about cloud costs and data security, among others. There are ten fundamental challenges that businesses must face in their cloud migration, and this article takes a closer look at each, and what can be done to overcome them.

1. Manage resistance to cloud adoption

The first, and arguably biggest challenge, is one that is as old as humankind itself: resistance to change. The bigger the disruption to the established way of doing things, the stronger the resistance. And cloud migration brings a significant level of disruption, with new systems, processes, and technology to be mastered. Ignoring the human aspects of cloud migration can subvert the best-planned migration strategies.

What can you do?

Prepare a change management plan and secure buy-in from key stakeholders. Obtaining support across the board, from C-suite management to department leaders can help mitigate a lot of the stress. Ensure also that everyone, from leadership downwards, understands the objectives and benefits of moving to the cloud. So communication is key. Finally, prioritize integration and usability. Employees are easier to get on board when they use and understand the benefits of the applications.

2. Clear uncertainty regarding cloud migration costs

Done correctly, cloud migration is sure to reduce costs significantly, while improving productivity and streamlining workflows. But it is not an overnight process. There are factors such as compatibility, new skills, data recovery, and, above all, the migration strategy that can add to your costs.

What can you do?

Preparation is the best way to avoid unnecessary migration costs. Work out the expenses in collaboration with your cloud service provider, before committing to a migration plan.

Include costs such as platform & infrastructure costs, migration costs, cost of delays (slow adoption), cost of training post-migration, and hiring new resources. If you need a hand, reach out to AAIC. Our cloud migration consultants will be happy to help you predict possible costs and work out ways to lower your cloud TCO.

3. Bridge the skills gap

Do you hire new talent or up-skill existing resources? This is a million-dollar question, but a necessary one to ask and understand. Because people worry that they might lose their jobs to migration. And this is a valid worry. Cloud migration needs different skills from those that are needed to run a legacy system.

What can you do?

The best way to deal with this issue is to do both: hire new talent to help with the migration and enhance your existing team’s skills. You need your in-house engineers, who know your applications and how they run. Up-skilling can help take a lot of the pressure off the rest of the team. You also need an external Cloud Migration Consultant, who will bring the necessary knowledge and skills to streamline the migration. Above all, everyone must understand the need for a collaborative approach. You can speed up the process by creating awareness about the skills and education needed. Set up digital boards with helpful information about certifications, et al, and reward individuals who earn needed certifications.

4. Application ecosystem & discovery tools

No application works discretely, each is part of an ecosystem, and you need to trace linkages to understand what connects with what in order to migrate the applications painlessly. This means you need discovery tools. Discovery tools like Cloudamize and other agent-based or agentless tools can connect data, estimate cloud servers, create architecture diagrams and reveal active executables.

What can you do?

Working without these tools means relying on expensive and slower manual processes to analyze business-critical applications, their structure, and dependencies. Finally, understand that you won’t be able to have each and every one of your features documented. In truth, this is one of the reasons for your migration—to review your application ecosystem and improve functionality.

5. Plan

Cloud adoption can be complicated. Moving your business infrastructure and assets calls for detailed planning. All too often businesses jump on the cloud bandwagon without taking the time and effort to plan how they adoption will take place. For instance, app modernization or refactoring vs lift and shift approach. All work, but each one suits a specific type of workload/application. While the lift-and-shift approach may be the fastest, it may not be the ideal one.

What can you do?

Start by brainstorming and putting together a detailed strategy for your cloud migration. This means you must analyze your business objectives and current assets, so you can pick the best-fit strategy. A good strategy should take you through the migration smoothly—no analysis paralysis!

Start by factoring in the applications that need to move, prepare a priority list, and calculate the cost of data processing, data movement, and cost of downtime during the transition. Be prepared to rebuild systems that might need it, once the migration is done. Finally, map milestones and plan steps to streamline the transition.

6. Managing Your Cloud Infrastructure

When you migrate to AWS you get access to a range of services and tools designed to make management of your infrastructure and data simpler. This is true in theory. In practice it can be a daunting task, especially in light of the AWS’s approach of continuous innovation, which releases new services and tools all-too-frequently.

What you can do?

Working with a top AWS Managed Services provider gives you access to the latest tools and processes from AWS, which are available exclusively to members of the Amazon Partner Network (APN). This gives you a range of advantages where management of your cloud is concerned.

At Applied AI Consulting, for instance, our architects and cloud engineers bring a high level os skills to manage, automate, and monitor your cloud workloads. This includes:

Change Management: helping you incorporate changes smoothly; this includes automation
Incident Management: Continuous monitoring of your cloud infrastructure
Patch Management: application of patches without disrupting your business
Access Management: Through Amazon Identity & Access Management (IAM)
Security Management: Securing your data and applications
Continuity Management: Backup for seamless restoration, in the event of failure
Continuous Improvement: Through monthly reports and recommendations we help you generate the maximum value from your AWS cloud.

7. Data Security and Compliance

Concerns about data security and compliance risks can pose serious challenges to your cloud migration. It’s natural to worry about handing your confidential data to third party security vendors—even in deeply secured cloud environments. Data breaches that occur during migration can lead to severe service disruptions.

What can you do?

Be aware that cloud platforms are built to be secure; their security systems are industry grade. Data, during migration, is encrypted and secured behind firewalls. This virtually eliminates the risk of data theft. For your peace of mind, though, you can query your cloud vendor, as follows:

What’s the data migration process?

Where is the data going to be stored?

Will the data be secured by end-to-end encryption?

Which security standards are the platform compliant with?

8. Prepare for DevOps transformations

This is a typical scenario: You want to implement a DevOps transformation on top of your cloud migration. This is a big disruption with lots of room for failure. The biggest of these is the culture, viz, DevOps Culture.

What can you do?

If you don’t create and encourage a DevOps culture within your organization, you will find it an uphill task to maximize cloud benefits. What is most likely to happen is that you will create an environment similar to your on-premises one, which means you will not realize all the benefits of cloud migration.

9. Adopt cloud-native architecture

There is no point in migrating to the cloud if you insist on using your clunky legacy applications. Going cloud-native means developing lightweight, disposable applications that easily scale out and are easy to monitor. Start by establishing a DevOps culture and using practices like container adoption, microservices, and serverless computing to build and deploy applications that allow for quick release and better scalability.

What can you do?

Gain an in-depth understanding of the twelve-factor principles and use them to set a baseline for adopting and building cloud-native applications. The twelve-factor approach describes a set of twelve best practices, originally drafted by Heroku in 2011, to develop applications deployed as services on cloud platforms.

10. Prepare for working in a hybrid setup

Chances are you will be working with hybrid networking; in which case, you must factor in security and networking right at the beginning. Hybrid configurations require security, which may not be required after the migration is successfully completed.

What can you do?

Define your minimum viable cloud— automation-, operations-, and security-frameworks, networking architecture, and core account structure. Implement identity and access management and define access patterns and plan how you will deal with related threat vectors. You may also need to boost security measures if you have a higher volume of personally identifiable information (PII) to secure. Plan how you will prevent data loss, patch vulnerabilities, etc.

In Conclusion

Cloud migrations can be challenging, but dealing with the challenges is easier when you’re prepared for them. Applied AI Consulting (AAIC) offers step-by-step guidance to help businesses unlock the full transformative power of the cloud. Interested in migrating to the cloud? Talk to AAIC’s Cloud Solutions Architects today to clarify any queries you may have about the cloud process.

Why should UI developers learn UI design?

Thu, 23 Feb 2023 05:42:00 +0000

As a UI developer, it’s easy to focus solely on the technical aspects of building a website or application. However, understanding the principles of UI design can enhance your abilities as a developer and improve the overall user experience.

Here are a few reasons every UI developer should attempt to learn about UI design.

Better Collaboration with Designers: When UI developers understand UI design, they can communicate more effectively with designers and provide insights into the technical feasibility of design concepts. This can lead to a more streamlined and efficient design process, resulting in better collaboration and more successful projects.
Improved Problem-Solving Skills: Understanding UI design helps developers think more creatively and critically about user experience problems and find solutions that both look good and work well. This type of holistic thinking is essential for creating effective and user-friendly interfaces.
Improved Code Quality: By incorporating design principles into their development process, developers can write cleaner, more maintainable code that is easier for other team members to understand and work with. This can save time and increase productivity in the long run.
Career Advancement: Knowing both development and design skills can make UI developers more valuable to employers and open up opportunities for leadership and management roles. It also allows for greater flexibility in finding job opportunities, as many companies are looking for individuals who have a well-rounded skill set.
Better User Experience: When developers understand UI design, they can ensure that the user interface they create is user-friendly, intuitive, and aesthetically pleasing. This leads to a better overall user experience, which can result in increased engagement, loyalty, and conversions.

2023: The Battle of Content Marketing Platforms begins

Fri, 13 Jan 2023 10:56:00 +0000

As online presence and digital marketing continue to be top priorities for businesses, the need for efficient and effective content management platforms has never been higher.

How do you ensure that your content recommendations stand out? The answer is to use the right tools and technologies to make content for your audience that is personalized, relevant, and interesting.

Enter Neo4J, the world’s leading graph database management system. Developed by Neo4J, Inc., this powerful tool is used by developers, data scientists, and enterprises worldwide to drive high-performance, scalable analytics, intelligent app development, and advanced AI/ML pipelines.

So, what is a graph database, and how can it enhance your content marketing efforts? Simply put, a graph database is a database that stores data in the form of nodes (entities) and edges (relationships). These nodes and edges can be used to represent anything from people and products to events and locations, making it an excellent choice for storing and querying complex, interconnected data.

Traditional platforms often rely on manual curation and scheduling, leading to delays and missed opportunities for engagement with consumers. However, by integrating Neo4j and ChatGPT, modern content management platforms can take advantage of predictive content creation and real-time responses, enhancing the customer experience and driving increased value for existing clients.

Neo4j, a leading graph database management system, allows for the storage and processing of complex data relationships in a highly scalable and efficient manner. By incorporating network structures and leveraging machine learning algorithms, businesses can gain valuable insights and make more accurate predictions based on their existing data. This is particularly relevant for content marketing, as it allows platforms to effectively target specific audience segments and personalize the messaging and content delivered to them.

Meanwhile, ChatGPT is a state-of-the-art natural language processing tool that enables real-time responses to user inquiries and comments. This not only enhances the customer experience by providing timely and relevant responses, but also frees up valuable resources for businesses by automating routine tasks. By combining the power of Neo4j and ChatGPT, content management platforms can deliver a more personalized and seamless experience for their users.

TWEET: ChatGPT not only enhances the customer experience by providing timely and relevant responses, but also frees up valuable resources for businesses by automating routine tasks. Must for Content Marketing Platforms. .

By analyzing user data and behavior patterns, these platforms can predict the type of content. This can significantly improve user retention and engagement, leading to increased revenue and growth for the platform.

One way to use Neo4J in your content marketing is by using it to create personalized recommendations for your audience. By analyzing data on their past interactions with your brand, you can use Neo4J to make recommendations for content, products, or services that are most likely to interest them. This could be in the form of a personalized email campaign, a targeted social media ad, or a suggested article on your website.

Another way to use Neo4J in your content marketing is by leveraging its powerful machine learning capabilities. By analyzing data on your audience’s behavior, you can use Neo4J to create predictive models that can help you understand what kind of content is likely to engage them most. This could be used to optimize your email subject lines, identify the most popular topics for your blog, or even predict which products your audience is most likely to purchase.

However, implementing and integrating these technologies can be a daunting task for businesses with limited in-house expertise in the field of artificial intelligence and machine learning. This is where Applied AI can provide valuable support and guidance. Our team of experienced professionals can help design and implement customized solutions that leverage the capabilities of Neo4j and ChatGPT, enabling businesses to maximize the value of their content management platforms and drive success in the digital world.

With Neo4J, companies can better understand their customers’ preferences and behavior, allowing them to create more targeted and personalized content. This can be particularly useful in industries such as e-commerce, where understanding customer preferences is crucial to successful marketing campaigns.

Overall, Neo4J is an incredibly powerful tool for enhancing your content marketing efforts. Whether you’re looking to create predictive models, make recommendations, or respond to your audience’s needs in real-time, Neo4J can help you achieve your content marketing goals.

In summary, the integration of Neo4j and ChatGPT can revolutionize modern content management platforms, enabling predictive content creation and real-time responses for enhanced customer engagement. By working with the team at Applied AI, businesses can confidently navigate the complexities of AI and machine learning to realize the full potential of these cutting-edge technologies.

What is Application Modernization

Fri, 30 Dec 2022 09:27:32 +0000

Application modernization is an important topic, as many businesses are looking to modernize their applications in order to stay competitive and meet the changing needs of their customers. There are many different aspects of application modernization, including updating the technology, improving the user experience, and optimizing performance. Some potential topics for blogs on application modernization could include:

The benefits of modernizing applications, including increased efficiency, reduced costs, and improved performance
The different approaches to application modernization, such as re-platforming, refactoring, or re-architecting
Case studies or examples of successful application modernization projects
Tips for planning and implementing an application modernization project

Re-platforming

What is re-platforming? What does it mean to modernize an application using re-platforming?

Re-platforming is the process of moving an application from one platform to another. This can involve changing the operating system, programming language, or underlying infrastructure that the application runs on. Re-platforming is often done as part of an application modernization effort, as it can help to improve the performance, scalability, and maintainability of the application.

There are a few different reasons why a business might choose to re-platform an application:

To take advantage of new features or capabilities: For example, a business might re-platform an application to a newer version of an operating system or programming language that has more advanced features or capabilities.
To improve performance or scalability: Re-platforming to a more powerful or efficient platform can help to improve the performance and scalability of an application.
To reduce maintenance costs: Maintaining an older or outdated platform can be expensive, so re-platforming to a newer platform can help to reduce maintenance costs.

Re-platforming can be a complex and time-consuming process, as it involves moving all of the code and data from one platform to another. It’s important to carefully plan and test the re-platforming process to ensure that the application continues to work correctly on the new platform.

Re-platforming can be best understood with a few examples. Let me share one such example.

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Re-factoring

What is re-factoring? What does it mean to modernize an application using re-factoring?

Re-factoring is the process of changing the internal structure of an application without altering its external behavior. This means that the application will continue to function the same way for users, but the code and design of the application will be improved to make it more maintainable, scalable, and efficient. Re-factoring is often done as part of an application modernization effort, as it can help to improve the long-term sustainability and performance of the application.

There are a few different reasons why a business might choose to re-factor an application:

To improve code quality: Re-factoring can help to remove duplicated code, improve code readability, and reduce the complexity of the application, which can make it easier to maintain and improve in the future.
To improve performance: Re-factoring can help to optimize the code and design of the application, which can improve its performance and scalability.
To make the application easier to maintain: By improving the code and design of the application, re-factoring can help to make it easier to maintain and update in the future.

Re-factoring can be a time-consuming process, as it involves making changes to the code and design of the application. It’s important to carefully plan and test the re-factoring process to ensure that the application continues to function correctly and that the changes are effective.

Lets look at an example of refactoring to understand it better.

Re-architecting

Re-architecting is the process of redesigning the architecture of an application. This can involve making significant changes to the way that the application is structured, including changes to the technology stack, data model, and overall design of the application. Re-architecting is often done as part of an application modernization effort, as it can help to improve the performance, scalability, and maintainability of the application.

There are a few different reasons why a business might choose to re-architect an application:

To take advantage of new technologies: Re-architecting can allow a business to incorporate newer or more advanced technologies into the application, which can improve its performance and capabilities.
To improve performance or scalability: Re-architecting can help to optimize the architecture of the application, which can improve its performance and scalability.
To make the application easier to maintain: By redesigning the architecture of the application, re-architecting can help to make it easier to maintain and update in the future.

Re-architecting can be a complex and time-consuming process, as it involves making significant changes to the structure of the application. It’s important to carefully plan and test the re-architecting process to ensure that the application continues to function correctly and that the changes are effective. It may also be necessary to retrain staff or update documentation and processes to reflect the new architecture of the application.

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Which AWS resources are used for replatforming, refactoring, rearchitecting?

There are a variety of AWS resources that can be used in the process of re-platforming, re-factoring, or re-architecting an application. Some of the resources that may be relevant to these processes include:

Amazon Elastic Compute Cloud (EC2): EC2 is a web service that provides resizable compute capacity in the cloud. It can be used to host applications that are being re-platformed, re-factored, or re-architected.
Amazon Relational Database Service (RDS): RDS is a web service that makes it easy to set up, operate, and scale a relational database in the cloud. It can be used to store data for applications that are being re-platformed, re-factored, or re-architected.
Amazon Elastic Container Service (ECS): ECS is a fully managed container orchestration service that makes it easy to run and manage containerized applications in the cloud. It can be used to host applications that are being re-platformed, re-factored, or re-architected, especially if those applications are designed to run in containers.
Amazon Elastic Container Registry (ECR): ECR is a fully managed container registry that makes it easy to store, manage, and deploy Docker container images. It can be used to store and manage the images for applications that are being re-platformed, re-factored, or re-architected, especially if those applications are designed to run in containers.

These are just a few examples of the AWS resources that may be relevant to re-platforming, re-factoring, or re-architecting an application. There are many other AWS resources that could be useful depending on the specific needs and requirements of the application and the process being used.

If I am replacing MSSql with open source PostGres – which category does that fall under? Replatforming, refactoring or re-architecting?

Migrating from MSSQL to Postgres would likely fall under the category of re-platforming, as it involves moving an application from one platform (MSSQL) to another (Postgres). Re-platforming typically involves moving an application from one operating system or infrastructure to another, and it can involve changes to the technology stack, data model, and overall design of the application. In the case of migrating from MSSQL to Postgres, the database platform would be changing from a proprietary database management system to an open-source one.

Re-factoring and re-architecting are also possible approaches to modernizing an application, but they typically involve making changes to the internal structure of the application rather than moving it to a new platform. Re-factoring involves changing the code and design of the application to make it more maintainable and efficient, while re-architecting involves redesigning the architecture of the application. Depending on the specific needs and goals of the modernization effort, any of these approaches (or a combination of them) could be appropriate.

To see more details of migrating from MsSQL to PostGres, click here. This shows how $1.4m were saved by this migration.

Serverless: If I want to modernize my application using Serverless technology, what would that typically entail? How much cost would I save using Serverless?

Modernizing an application using serverless technology typically involves making changes to the way that the application is hosted and managed. Serverless technology is a type of cloud computing that allows businesses to run their applications without having to worry about managing the underlying infrastructure. Instead of running applications on physical servers or virtual machines, businesses can use serverless technology to run their applications in a fully managed environment that is scalable and cost-effective.

To modernize an application using serverless technology, you would typically need to do the following:

Identify the parts of the application that are suitable for running in a serverless environment. This might include workloads that are event-driven or have variable usage patterns, as serverless technology is particularly well-suited for these types of workloads.
Rewrite or refactor the application code as necessary to run in a serverless environment. This might involve breaking the application into smaller, independent functions or changing the way that the application handles data and resources.
Deploy the application to a serverless platform, such as AWS Lambda or Azure Functions. This will typically involve creating and configuring the necessary resources, such as functions, event sources, and execution roles.
Test and monitor the application to ensure that it is functioning correctly and meeting the needs of your business. This might involve setting up monitoring and logging tools and establishing a process for continuous integration and delivery.

Modernizing an application using serverless technology can have many benefits, including reduced costs, improved scalability, and easier maintenance. It’s a powerful tool for businesses looking to improve the performance and efficiency of their applications.

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How much cost would I save using Serverless technologies?

It can be difficult to predict exactly how much cost you would save by using serverless technology, as it depends on a variety of factors, including the size and complexity of your application, the resources that it consumes, and the pricing model of the serverless platform that you use.

In general, serverless technology can help businesses save on costs in a few different ways:

Pay-per-use pricing: With serverless technology, you only pay for the resources that you use, rather than paying for a fixed amount of capacity upfront. This can help to reduce costs for applications that have variable usage patterns or that experience spikes in traffic.
No need to manage infrastructure: With serverless technology, you don’t have to worry about managing the underlying infrastructure, such as servers or virtual machines. This can help to reduce costs associated with infrastructure management, such as maintenance, patching, and scaling.
Automatic scaling: Serverless platforms can automatically scale your application up or down based on demand, which can help to optimize costs. For example, if your application experiences a sudden spike in traffic, the serverless platform will automatically provision additional resources to meet the demand, and then release those resources when the traffic subsides.

Overall, the cost savings that you can achieve with serverless technology will depend on your specific use case and the needs of your application

By moving to serverless, will I save more on people cost or on infrastructure cost?

By moving to serverless technology, you may be able to save on both people costs and infrastructure costs. Here are a few ways that serverless technology can help businesses save on these types of costs:

People costs: By using serverless technology, you can potentially reduce the number of people required to manage the underlying infrastructure of your application. This is because serverless platforms handle tasks like infrastructure management, scaling, and patching automatically, so you don’t have to allocate as many resources to these tasks.
Infrastructure costs: With serverless technology, you only pay for the resources that you use, rather than paying for a fixed amount of capacity upfront. This pay-per-use pricing model can help to reduce costs for applications that have variable usage patterns or that experience spikes in traffic. Additionally, because serverless platforms automatically scale your application up or down based on demand, you may be able to save on costs associated with overprovisioning or underprovisioning resources.

Ultimately, the specific cost savings that you can achieve with serverless technology will depend on your specific use case and the needs of your application. It’s a good idea to carefully assess your current costs and usage patterns to understand how serverless technology could impact your overall cost structure.

What are the factors that could impact the costs and benefits of modernizing an application?

There are a number of factors that could impact the costs and benefits of modernizing an application, including the specific needs and goals of the business, the current state of the application, and the specific modernization approach that is taken.

To make a more informed decision about whether to modernize an application, it would be helpful to have a clearer understanding of the current costs and benefits of the application, as well as the expected costs and benefits of modernizing it. For example, it would be helpful to know:

What are the current costs of the application, including people costs (e.g., salary, training, and development) and infrastructure costs (e.g., servers, storage, and networking)?
What are the current benefits of the application, including revenue, efficiency, and customer satisfaction?
What are the expected costs and benefits of modernizing the application, including the cost of the modernization effort and the ongoing costs of running the modernized application?

Once you have a clearer understanding of these factors, you can then compare the expected costs and benefits of modernizing the application to the current costs and benefits to determine whether it makes sense for your business to proceed. You may also want to consider other factors, such as the level of risk associated with the modernization effort and the impact that the modernization could have on your business’s operations and competitive position.

Is it a good idea to get the application assessed for modernization via a third party consultant or should the assessment be done in-house?

There are pros and cons to both approaches, and the best option for your business will depend on your specific needs and resources. Here are a few factors to consider when deciding whether to get your application assessed for modernization via a third party consultant or to do it in-house:

Expertise: A third party consultant may have more expertise and experience in assessing applications for modernization than your in-house team. This can be especially beneficial if your in-house team is not familiar with the specific technologies or approaches that are relevant to the modernization effort.
Objectivity: A third party consultant may be able to provide a more objective perspective on the modernization potential of your application. This can be helpful if you are looking for an unbiased assessment of the strengths and weaknesses of your application.
Time and resources: Assessing an application for modernization can be a time-consuming and resource-intensive process. If your in-house team is already stretched thin, or if you don’t have the necessary expertise or resources to conduct the assessment in-house, hiring a third party consultant may be a more efficient option.
Cost: Hiring a third party consultant can be more expensive than conducting the assessment in-house. However, the cost of the assessment may be offset by the cost savings or revenue gains that result from the modernization effort.

Ultimately, the decision of whether to get your application assessed for modernization via a third party consultant or to do it in-house will depend on your specific needs and resources. It may be helpful to weigh the pros and cons of each approach and to consider the specific goals and constraints of your business when making this decision.

Contact AAIC for assessing your app. First 30 minutes free.

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Understanding AWS IAM

Wed, 21 Dec 2022 10:48:00 +0000

Architecting your environment in AWS—or any cloud, for that matter—is just the beginning. Like any other asset, it must be managed. But the typical cloud has many moving parts, i.e. services that need to be run, monitored, and managed. The right thing to do, of course, is to delegate tasks. But delegating tasks means assigning permissions, which can be misused—wilfully or otherwise—so these too need to be managed. AWS Identity and Access Management (IAM) is a service that allows you to do just that. IAM enables you to control access to all the AWS resources in your infrastructure as well as those you might need going forward. IAM allows you to control who is allowed to use which resources.

When you first create your AWS account, you start with a sign-in id. This identity, called the AWS root user, consists of the email address and password you used to register for the account. AWS strongly recommends that the root user credentials are properly safeguarded and used only to perform certain tasks, viz, which only the root user can perform. The full list of these tasks can be accessed in AWS documentation, at

https://docs.aws.amazon.com/

IAM Features

Shared access

IAM allows you to give other people permission to use and administer resources in your AWS account without sharing your password or log-in.

Fine-grained access

You can give different people different permissions for specific resources. For instance, you can give some people access to Amazon EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), DynamoDB, and other specified AWS resources, you can grant read-only permissions to administer certain EC2 instances, or access only certain S3 buckets. You can even allow outside users who might already have passwords elsewhere (with an internet identity provider, like Facebook or Google, for instance) to gain access temporarily to your AWS account.

Authenticating IAM Users & Groups

All users must be authenticated before using any AWS services, which can be accessed in the following ways:

Through the AWS management console—a browser-based interface

Through the AWS command line interface and command line tools—this is faster and more convenient than using the console. The tools can be used to write scripts that perform AWS tasks

Using AWS software development kits (SDK) These kits are provided by AWS and consist of libraries for a variety of programming languages and platforms like Python, Ruby, .Net, Android, iOS, etc

IAM HTTPS API—access IAM programmatically by issuing HTTPS requests, but you must include code to digitally sign requests with your credentials

Users and User Groups

Each user has specific permissions with access to specific resources to perform specific tasks. You can create users in your AWS account to match people in your organization.

As the number of users managing your account grows in number, you might find it easier to manage permissions for multiple IAM users using IAM Groups. Each member of the group will have the same permissions.

After a user has been authenticated, they need to be authorized to use certain resources. This is because by default users are granted access to anything in your AWS account. You need to manage this by creating a policy. You can assign the policy to an individual user or a group. When you assign a policy to a group, all the members in the group get the same permissions by default.

IAM Policies

An IAM policy is a JSON document that lists permissions explicitly. Any actions or resources that are not granted in the document are denied to the user. The IAM policy tells you:

Who is authorized? This could be a user/group or another user in AWS

Which task is allowed?

What conditions must be met for authorization, e.g. IP address?

The resources on which the authorized tasks can be performed

IAM Roles allow users to assume certain permissions temporarily in order to undertake specific tasks

IAM Roles

An IAM role is an identity that is linked to resource-based policies.

It allows you to grant access to users or services that wouldn’t normally have access to your AWS resources. These users or services can temporarily assume the specific role and get the security credentials needed to make AWS API calls.

It is similar to a user, in that it grants the user permission—or denies permissions—to do certain things. Roles do not have any long-term credentials like access keys or passwords; these are created dynamically when a user is assigned to a certain role. Note that a user who assumes a role gives up his/her own permissions and assumes the permissions granted with the role. Roles offer a secure way to pass credentials temporarily to a user so they can complete a certain job.

Takeaway

AWS IAM lets you create users and groups to help you manage your AWS cloud. You can manage IAM roles and their permissions using policies that specifically grant or withhold permissions to take certain actions on certain resources. When you attach a policy to a user group, all the members of that group assume the same permissions. You can also grant temporary permissions using IAM roles. IAM also enables the use of identity federation to allow existing entities in your enterprise to access your AWS account without creating separate IAM policies for each identity.

What is DevOps

Wed, 21 Dec 2022 10:31:53 +0000

Not everyone has the same idea about DevOps. For some it is a culture, others describe it as processes or practices, for others is a set of tools. Part of the problem is that the term itself is so broad and includes so many factors that it is not easy to clearly define it or set its boundaries.

The simplest—and probably the most popular—definition describes DevOps as the intersection of Development and Operations. The problem arises when we try to set the boundaries of where Development ends and Operations takes over and vice versa.

The Need for DevOps

To begin with, why do we even need something like DevOps? To understand this, we need to look at the release process.

When you develop an application, the end goal is always the same—no matter whether the team follows a waterfall strategy or agile or any other—and that is to deliver the application to the user.

Say, you have an idea for an application. You will bring it to life by first defining its functionality and setting out its features, then you will code the application, following which, you will test it, and finally host it on a server—probably a public server along with all the security it needs, viz. firewalls, access rules, etc. This is a stripped-down description of the steps any application deployment needs. But the story doesn’t end here.

You still need to monitor your application and check whether it is running as desired: are there any bugs, can the architecture handle high traffic loads, are users facing any problems? If any issues crop up, you, of course, need to fix them. Even if everything is fine, you may want to add new features, and further improve performance: maybe by provisioning better servers or making the application faster. But this isn’t the end… there’s still lots to do. And every time you make any changes to the application, be it the code or underlying infrastructure, you need to redeploy it to make it accessible to end users. Now there will be multiple iterations post the initial launch and you need to keep track of the updates, so you also need to establish a versioning system and keep track of them. The cycle of launch-update-relaunch is an endless cycle and DevOps helps streamline this by establishing a continuous delivery process. DevOps, in short, enables you to make endless improvements to your application, test the changes and deliver the updated application to your end users without interruption.

Challenges

There are several bottlenecks and roadblocks that slow down the deployment process and they can be listed as under:

Lack of collaboration

Lack of collaboration between developers and Operations is the biggest challenge. These are actually separate aspects of the process. Developers are only concerned with creating (coding) the application and Operations personnel are responsible for running the application. Operations are running the application but often aren’t aware of how it works, so any attempt to tinker with the code is a recipe for disaster. Typically, developers will complete the coding but the documentation may be incomplete or just not good enough for the Operations team to deploy. If there are many usage issues, Operations simply throws it back to the Development team with improvement recommendations. All this means release takes longer; sometimes days and weeks and maybe even months.

Checklists as bottlenecks

Sometimes handover from developer to Operations is governed by bureaucratic checklists that set out what needs to be completed, who needs to approve what, and so on. The problem with this is that it results in further delays as a lot of these tasks must be completed manually, checked, and verified before handing over.

Differentiated goals

Developers are primarily focused on pushing out new features fast. This is their objective, whereas Operations want to ensure the application runs in a stable fashion. To ensure this, the Operations team, traditionally, tends to delay the release until all the i’s have been dotted and the t’s crossed. Since Operations usually isn’t familiar with code the process is a slow one. For instance, let’s say a new set of features has been developed but these features end up consuming so many resources in production that servers get overwhelmed and the application crashes. Now Operations needs to get it up and running again. Since fire-fighting is Operations’ responsibility, Developers are not are quality conscious as they should be, focusing more on the speed of churn than stability Even though it is the common goal of everyone in the organization to deliver high-quality applications to the end-users, quickly, in practice, there is conflict as both teams focus on their immediate goals.

Security challenges

Just as the Operations team evaluates the changes to make sure it won’t break anything in a production environment, so too must the Security team evaluate changes to ensure they don’t impact security systems. The only problem with this is when done in a traditional setup, this process is another bottleneck as it involves the same kind of bureaucratic process as we saw in the Operations check. So it will take another couple of days or weeks, further slowing down the deployment process. When DevOps incorporates automation of security checks, it is known as DevSecOps.

Manual processes

In a traditional setup, many of the tasks that are part of the deployment process are done manually and this slows down things considerably. For instance, Operations tasks are done manually by directly executing commands on the servers, patching, writing and running small scripts/programs, manually creating deployment environment and infrastructure, configuring access to servers, manually configuring Jenkins, and so on. All this manual effort is not just time-consuming, it is also prone to errors, and it is very hard to trace who executed what and when. So if something were to happen to the infrastructure, it would be very difficult to replicate the exact state quickly.

DevOps aims to resolve all these and focus on creating a fully automated streamlined process. We do this by removing the bottlenecks, one at a time, until we have a fully-optimized process that makes application releases easy and so fast, you can have multiple releases a day, instead of days or weeks, or months, like previously.

Defining DevOps

Per the official definition, DevOps is a combination of

cultural philosophies
Practices
Tools

All of which aim to make the release process error-free and speedy.

The traditional definition of DevOps slowly evolved as the actual implementation of DevOps by individual companies was not uniform, but it eventually developed concrete patterns, and one of the changes that came from this was the creation of a specific role: the DevOps Engineer. The DevOps engineer could be either a developer doing DevOps or an Operations engineer only doing DevOps or a separate role. But the sole job responsibility would be to focus only on creating a streamlined process for release. And this process centers around the familiar process known as CI/CD, or continuous integration, continuous delivery/deployment.

What every DevOps Engineer Should Know

While DevOps engineers may not be working on the actual programming of the application, they need to understand versioning and how developers work, and how to prepare an application for release, this includes:

Understanding how Git works, which Git workflow the development teams are using, how the application is configured, tested, etc.
How the application is configured to interact with other services/databases

automated workflows

Development of underlying Infrastructure, i.e. creation and configuration of servers, be it on-prem or in the cloud. This means proficiency with Linux servers, as these are the most commonly used ones.
How to set up, and administer a Linux server, Linux commands, and Linux file system so the developer can use the Command Line Interface
Basics of networking and security…configuring firewalls, how IPs address ports, opening some ports so the application can be accessed from outside; DNS and how it works

A DevOps engineer needn’t have advanced operating system skills or networking and security skills, or even how to administer servers from end-to-end. There are already specialists for these tasks. The DevOps engineer’s job is to understand all of the above only to the extent needed to prepare the server to run the application, rather than take over management of the servers and infrastructure.

Containers: As containers have become the de facto infrastructure nowadays, the DevOps engineer needs to understand how to manage containerized applications. This means acquiring proficiency in popular container tools, like Docker. This means how to build docker images from your application, and save these in a Docker image repository, like Nexus or Docker hub.
CI/CD pipelines and associated software

DevOps job is basically to release new application versions, fixes, etc, in a continuous and efficient, and automated way. To do this the DevOps engineer needs to know how to run tests, package the application as a jar or zip file, and deploy it. So they need to know software like Maven, Gradle (for Java), and NPM (for Javascript apps).

Now, this doesn’t have to be done manually, instead, the DevOps engineer needs to build an automated pipeline so all of the above is done in a sequential manner. This means knowledge of Jenkins, which is the most popular build automation tool. Next is connecting the pipeline to the git repository to get the code. This is the key part of the Continuous Integration process. Next is continuous testing and bug fixes if needed before deploying the new feature or bug fix. And this is Continuous Deployment. Along the way, it should also send notifications to the respective teams about pipeline state and failed deployments.

Continuous Integration/Continuous Deployment pipeline

In a Nutshell

Basically, DevOps connects Development and Operations teams. While there is no need to be a master in either of these areas, some basic knowledge of both is needed. Above all, DevOps engineers need to know all the tools and practices needed to build automated pipelines that can get the code for the new releases, test it, fix it—if there are bugs—and deploy it. This is a continuous process which is why the symbol for DevOps has become the infinity symbol, showing movement from Development to Operations in a continuous cycle.

Choosing between SQL and NoSQL; Amazon Aurora and DynamoDB

Tue, 29 Nov 2022 12:42:31 +0000

Everyone who has worked with databases is probably familiar with relational databases, and the crown jewel of relational database management systems (RDBMS) is SQL. But over the years as data volumes have exploded, and storage & processing power with it, a new type of database also evolved along with it. A database that could collect this new and vast volume of data in its raw format and analyze it faster than traditional means. This is NoSQL. Thus the two broad categories of data can be described as SQL, or structured query language, and NoSQL, or non-structured query language. This article takes a closer look at the differences between both, and compares the two crown jewels of Amazon’s database offerings, Amazon Aurora and DynamoDB

Structure

SQL is by no means obsolete, it is still widely used and is excellent for querying relational languages, and this is the main differentiator between the two. SQL databases use fixed schemas to store data. NoSQL databases are schema-less. This can be better understood with an example, say you have to save data related to an address book. The table you will probably use will have a definite structure: say, names in one column and other parameters, like address, email id, tel number, etc in different rows. Now say you suddenly need to add a row for hobbies; this is not possible, as there is no row defined for this in the schema. And this is where schema-less tables score over SQL. In a similar use case as the one described for SQL, you do not need to add a predefined schema for the new information. Each row is simply a repository for different information. So, for example, you can have the name, age, hobbies, and email in the first one, and name, sex, address, and telephone number in the next. Each one can hold different information. So the biggest difference between SQL and NoSQL is that the former is structured to have a defined schema and the second is schema-less and holds unstructured data. Note here that a NoSQL database can also hold structured data.

Types of NoSQL databases

There are four basic NoSQL types of databases: document databases, key-value databases, wide-column stores, and graph databases.

Document databases store data in documents, like JSON files, containing pairs of fields and values. Values can be objects, numbers, booleans, or arrays. Key-value databases store data in keys and values. Wide column types store data in tables, rows, and dynamic columns. Lastly, Graph databases store data in nodes, which can be information about people, places, things, etc, and edges, which store information about relationships between the nodes.

Schema vs Schema-less Pros and Cons

The big advantage of SQL databases and their predefined schema is that the data is better set up. The indexes, secondary indexes, foreign keys, are optimized and The database already understands the columns and rows and what is where, so it is easier to query. Thus, SQL databases are good for joints and complex queries, like those involving multiple tables. As there are predefined columns, foreign keys that the database understands, you can link an SQL database to multiple tables, so you can join tables and run sub-queries, etc. Schema-less NoSQL databases have different data for every item or row making it impossible to define the different keys that the NoSQL database supports. And the absence of a predetermined structure makes it unsuitable for complex multi-table queries.

Transaction Processing

In terms of processing transactions, SQL databases follow the ACID principles, where A-C-I-D is an acronym for:

Atomicity: where changes are performed in unity or not at all

Consistency: from the time a transaction starts till it ends, the data state is consistent

Isolation: the intermediate status of a transaction is invisible to other transactions

Durability: changes to data remain even if there is a system failure

NoSQL databases follow CAP or the CAP principle, which refers to

Consistency: when the nodes have the same data item and every node returns the same after a successful write. It implies that every client gets the same view of the data

Availability: when the system sends a successful/unsuccessful message after every read/write operation is completed

Partition tolerance: means the system continues operating even if connecting nodes in the network have a fault

Amazon Databases

AWS offers a choice of both types of databases. In SQL, you have Amazon Aurora and Amazon RDS; Amazon DynamoDB, Amazon DocumentDB (which is compatible with MongoDB), and Amazon Managed Apache Cassandra Service. But AWS also allows you to run any database you might prefer on EC2, although it is recommended that you pick one of AWS’s native databases preferably.

Vertical Scaling vs Horizontal Scaling

Amazon Aurora

Amazon Aurora is Amazon’s premier SQL database. It was released in 2015, and is based on the Relational Database Management System (RDBMS) model. It is a different beast than SQL but it supports SQL language. It is compatible with MySQL and PostgreSQL, but much faster than both.

Amazon Aurora uses a concept called Referential Integrity, which doesn’t use Foreign Keys. Referential Integrity requires that the values in the foreign key column must be present in the primary key that is referenced by the foreign key or the values must be null. This ensures the data in the database remains consistent. It keeps incorrect records from being added, deleted, or modified.

Where DR is concerned, Aurora allows backups and snapshots

To ensure high availability for your Amazon Aurora Database you can choose multiple AZs and read replicas.

Scaling in Amazon Aurora happens vertically. This means choosing a bigger/more powerful server, i.e. EC2. Although Serverless Aurora scales automatically.

Amazon DynamoDB

DynamoDB is a proprietary database developed by Amazon in 2012. It is a fully managed, scalable database service built for the cloud. Unlike Aurora it doesn’t use the concept of Referential Integrity; so, there is no Foreign Keys DynamoDB’s primary database model is a document store and a key-value store.

As with most NoSQL databases, it doesn’t support SQL query language; Unlike Aurora, which only supports the Master-Slave replication method, DynamoDB supports most replication methods.

It supports cross-region replication. This means that if you have a table in US East one, which is replicated in US West two, you can insert a row in either one of the tables, it will be instantly replicated in the other

It is highly available and resilient as it instantly replicates across three AZs.

DynamoDB scales horizontally. This means it spins out multiple read replicas to handle spikes in traffic. It can handle more than 10 trillion requests/day with peaks of more than 20 million/second. This makes it much more scalable than Aurora, although Serverless Aurora *does *scale automatically, it is not as scalable as DynamoDB

DynamoDB is inherently durable, enabling Point in Time Backups.

Choosing the ideal database doesn’t mean picking one or the other. Rather it’s a matter of picking the right tool for the job. For instance, say you’re in charge of a project and you choose to work with a NoSQL database. This doesn’t mean you have to force-fit everything to the NoSQL database, even if it would be a better match for SQL. With most modern applications being Microservices-based, the choice becomes easier, because microservices architectures are autonomous, in that you can pick SQL or NoSQL database, whatever fits the solution better. At the end of the day, the choice is not between SQL and NoSQL or Aurora vs DynamoDB but the actual case, performance levels, and cost requirements.

Making the Decision: EC2 vs. Lambda for Your Cloud Computing Needs

Thu, 24 Nov 2022 13:06:04 +0000

Servers have always been the backbone of all computing environments, be it on-prem or in the cloud. But servers need to be monitored and managed, updates need to be made regularly, the data needs to be stored properly, backed up, made accessible to the right people, and above all, secured. The coming of Serverless Computing took away all these responsibilities, saving on capital expenditures, and leaving organizations free to focus on their core business more freely.

Of course, the term ‘Serverless doesn’t mean there is no server; just that the server has been abstracted away by the cloud service provider, who assumes responsibility for its management and security. AWS, which changed the face of computing by pioneering cloud computing, has also given the most popular cloud server-led compute service forms, EC2, as well as, Lambda, a serverless compute service that you can use to host highly available and scalable applications. So which one is better? To find out, this article maps the differences between EC2 and Lambda Serverless. Remember though that Lambda is not AWS’s only serverless service, there are several others as we shall see. But they all promise the same benefits as Lambda.

EC2 vs Amazon Lambda

Infrastructure

The biggest advantage serverless computing offers is that you don’t have to provision or manage servers. This is done entirely by the CSP. So you don’t have any provisioning, and management headaches, patching, updates, etc. are all taken care of by the Cloud Platform. With EC2 instances, you control the underlying infrastructure. Thus, you need to take care of scalability, Amazon Machine Image (AMI—templatized configuration, i.e. containing application with OS, App server, etc.), rehydration (spinning up new servers, complete with the latest patches already installed, destroying old servers, etc)

This also means enormous savings in terms of capital expenses, which are converted into operating expenditures, as we will see.

Scalability is an implicit benefit of cloud computing, but with serverless computing, this happens automatically, unlike with EC2. As traffic to your application increases, the platform automatically spins up more Lambda instances to accommodate the traffic. With EC2 this doesn’t happen automatically. Users need to set up load balancers, auto-scaling groups, set up auto-scaling policies, etc., themselves.

Pay for what you use

Pay-per-use is another implicit benefit of cloud computing, but the benefits are significantly enhanced with serverless. As long your function doesn’t run, you pay nothing. With EC2 you still have to pay for your instances, whether they remain idle or not.

Availability

As you don’t need to manage any server, Lambda functions are highly available and scalable. Serverless functions are automatically deployed in multiple availability zones (AZ). You don’t have to do anything. This means that even if one of the AZs goes down, your Lambda will still be up and running. However, with EC2, the onus is on you to deploy it in different AZs, create load balancers—the whole kit and caboodle.

Lambda is not the only AWS serverless service. AWS offers Amazon DynamoDB, Fargate, AWS API Gateway, and Amazon SQS (simple queue service), among others. What this means is that all these services offer the benefits of serverless listed above.

Software

You can’t install software, for instance, web servers, or app servers, in the underlying environment; however, you can install whatever code libraries you need for your Lambda code to run. These can simply be zipped up and deployed. With EC2, since you control the underlying environment, you can install almost any software—there are several pre-packaged AMIs for a variety of different software available. Amazon EC2 supports a variety of different operating systems; with serverless services, AWS Lambda natively supports Java, Go, PowerShell, Node. js, C#, Python, and Ruby code, and provides a Runtime API, which allows you to use any additional programming languages to author your functions.

Compute

Lambda allows you to select compute power at any point. You simply shift the slider bar for memory and timeout. Memory can be ramped up from 128 MB to 3 GB, and timeout from a single second to 15 minutes. As you increase memory to your Lambda, the platform automatically allocates more CPU as well. This happens instantaneously, even while the Lambda is running. With EC2 instances, you can choose the size and type of VM instance. What you’re doing, in effect, is changing the EC2 instance itself, so there is a brief interruption.

Storage

Lambda doesn’t have any attached hard disk, so the deployment package size is limited. EC2 instances allow you to attach multiple hard disks or Elastic Block Storage (EBS). So you’re not limited by package size. You can pretty much deploy any size of package you require.

Cost

Factors like the cost of installing and maintaining hardware, IT staff, etc. make it more expensive to use an EC2. This is avoided with Lambda. Since you only pay for usage, this brings down costs considerably. Plus you avoid paying for hardware, IT staff, etc, however, what you gain in the elimination of management, you lose in control of your infrastructure.

Major Benefits at a Glance

Lambda (Serverless)

EC2

No need to manage server

complete control of your environment

highly scalable

Scalability & availability instances need to be provisioned and configured by users

highly available

Availability depends on architecture

no attached storage

Can deploy as much as needed

Cheaper when traffic is unpredictable

Steady, heavy traffic volumes suit EC2 adoption

Use Cases

1. Integration

Lambda is excellent for event-driven functions. It offers native integration with other services, like S3, and Kinesis. This means you can trigger lambda to run the object in the S3 bucket directly. With EC2, as we saw, since you control the underlying environment, you can run just about anything. Suppose you need to run a web server or app server with third-party software. You can install all of this in your EC2

2. Traffic (un)predictability

Lambda shines in use cases where traffic cannot be predicted. As it is inherently scalable and automatically highly available, you don’t have to configure anything. Furthermore, since you only pay for when the function actually runs, you avoid paying for any idle time

ECS works best when traffic can be predicted. Because you’re paying for all the underlying infrastructure, it doesn’t matter if it is idle or running. Furthermore, you pay for the entire VM, not just the part being used. Let’s say you’ve set up Elastic Load Balancer, Autoscaling Group with your EC2 running inside. As traffic increases, it spins up another EC2 instance to accommodate the increased traffic, but let’s say only 50% of the new EC2 is being used. You still pay for the entire VM. Clearly, the best use-case scenario for EC2 is when traffic is steady and can be predicted

3. Microservices adoption

Both EC2 and Lambda are well suited for microservices, but for different reasons. Lambda has API Gateway integration and, because the code is modular without dependencies on software, it is generally easier to migrate cloud-native greenfield applications. However, brownfield monolithic applications will need major refactoring. EC2 is also a good candidate for microservices, as you can easily move APIs with dependencies, although you do have to consider the cost and complexity of building and running a greenfield application on EC2.

Takeaway

There is no clear winner in the EC2 vs Lambda debate. As we have seen it all depends on your requirements, the need to control the underlying environment, storage/memory, traffic volume, predictability, etc. Serverless is clearly here to stay, but not all organizations will benefit from it. If you’re considering going serverless, but are not sure, talk to us.

Kubernetes Security 101: Understanding RBAC and Service Accounts

Thu, 24 Nov 2022 12:52:24 +0000

Kubernetes’ API only responds to requests that it can authenticate. This means that in order to properly secure your Kubernetes API, you need to secure your Role Based Access Control (RBAC) policies. RBAC is basically a mechanism that allows access to only users, i.e. as per permissions that have been set by you as admin. So how do you secure your application running on Kubernetes in AWS?

Let’s begin by revisiting the basics. Your application is running in a pot and to function properly you need to give the pod access to AWS resources, like creating a load balancer or attaching security groups. If your application were running in a standard Amazon EC2 instance you would simply attach IAM (Identity & Access Management) roles with policies allowing certain actions (for example, the ones mentioned above). Now since the pod is running in Kubernetes, you can’t simply attach an AWS-specific service like IAM, instead, you need to go back to the source, i.e. the Deployment Manifest. But the Deployment Manifest, too, is a Kubernetes construct, and as we noted, you can’t simply attach AWS cloud-specific constructs like IAM. Rather, we need to abstract such cloud-specific constructs. This will allow Kubernetes to run in other cloud platforms—AWS, in this case. This simply means inserting a layer in between…i.e. Service Account.

In a nutshell, the stack goes something like this: policies that allow access to resources that you specify. These policies will be attached to an IAM role, and this IAM role is attached to Service Account—which is a Kubernetes construct—and finally, this Service Account is connected to the Deployment Manifest. Since the Deployment Manifest-Service Account is running in AWS EKS, it needs a way to connect with AWS IAM service to validate the permissions and policies you have set. So what we do is use an IAM OpenID Connect (OIDC) service—an authentication protocol—to connect your policies to your cluster. This is set up at the cluster level, and only needs to be set up once.

Now, if you run “cubectl get sa account” you will get “default‘ in return; however if you run *cubectl sa -A *(A in Capital) it returns a list of all the Service Accounts in your Kubernetes cluster. For the purposes of this article, we will pick alb ingress controller. The alb ingress controller monitors ingress and interacts with the Kubernetes API server. When the ingress resource is deployed, it allows you to create Application Load Balancer Security groups, attach a Security Group, etc. The IAM Role associated with each Service Account is connected to a role name and in you look up the role name you can see all the policies connected with that role, e.g. EC2 security group, role to create load balancers, etc. Remember, the Service Account is attached to an IAM Role, and has nothing to do with the Kubernetes cluster or the pods in which your application is running. Now the pods—which are running the application need access to the Kubernetes cluster. This is where ClusterRole comes in.

Role & ClusterRole

Just as an IAM Role gives access to specific AWS resources, ClusterRoles give the pods access to resources on a cluster level. The roles allow you to create/delete/list on the nodes, pods, namespaces, and all other resources you need on a cluster level to run your application effectively in the pod. In other words, to run your application effectively you need access to both AWS resources as well as Kubernetes cluster resources. ClusterRole gives you access across the cluster; if you have Admin-level access you can do just about anything, which is why Admin ClusterRoles should only be used with caution.

Role binding grants permissions as defined in the role to a user. Permissions can be within a namespace or cluster-wide, the former is known as RoleBinding, and the latter, ClusterRoleBinding.

If you look at a typical manifest file, at the bottom you can see information about the Service Account creates, the name of the Service Account, and in which namespace it has been created. Above this, you can see the ClusterRole that has been created, under ‘Rules’ you can see which resources it has access to and the kind of actions this ClusterRole can take. To connect this ClusterRole with the Service Account, you have ClusterRolebinding in the center layer. The ClusterRole is referenced with the name specified.

ClusterRoles are reusable, i.e. they can be replicated for another namespace. All you need to do is create a Service Account in that specific namespace and reference the same ClusterRole to that Service Account.

Role is not an IAM construct, it is a Kubernetes term.

Both ClusterRole and role represent permissions. These are similar for both role and ClusterRole but role defines permissions with a specific, defined namespace, and works only on resources—such as pod, replicaset, and cluster—specific to that namespace.

ClusterRole, on the other hand, defines permissions across the cluster, i.e. not specific to a particular namespace. In a nutshell, if you put a namespace when you define a role it becomes a regular role; if you don’t put a namespace when defining the role, you’re not tying it to any specific namespace and it becomes a ClusterRole.

Role Based Access Control (RBAC)

There are two basic areas to secure in a Kubernetes application: one is your application’s security, and the second is security for you as the user. ClusterRole, Service Account, Role Binding, etc all were aspects to secure your application. Security for the human user consists of granular permission specific to the user type. For instance, for a DevOps person, you’d want to grant permissions, like give, create, get, and list, in a particular namespace or on deployments, replicasets, and pods. To do this, you will first create the Kubernetes role defining access to resources in that namespace. (This is a namespace-specific Role). Then you will map that Kubernetes username to the Role using RoleBinding. Finally, you need to map the AWS IAM username to the Kubernetes username and group using Configmap/AWS auth. Both Rules and ClusterRules are similar, so you can define API groups, resources verbs, etc.

You can have two namespaces running in the same node (or EC2 instance), with different pods, and each namespace running a different application. You can create a Service Account for each of the namespaces, tie each to an IAM Role and connect that to a cluster role specific to the corresponding namespace. Now each of the IAM Roles and Cluster Roles in the two namespaces can have totally different permissions and access, even though they are running in the same node/EC2. The reason for this is that if you put an IAM role associated with a particular node that has access to all the pods, you are, in effect granting permissions to all the pods running in that node.

Finally, please note that this concept is only available from Kubernetes version 1.14 onwards; it replaces Cube2IAM.

Exploring Container Orchestration Options with AWS ECS, EKS, and Fargate

Mon, 14 Nov 2022 06:20:55 +0000

There is no doubt that microservices came as a boon to developers in the cloud. And one of the reasons for their growing popularity is the innovation we know as the containers. Containers basically contain everything needed to run an application—such as the configuration, code, runtime engine, etc. Containers still need to be managed though. They need to be deployed, networked, unneeded replicas must be deleted when not needed, crashed containers must be restarted, etc. All of this is not possible to do manually. Remember even the most basic microservices app will comprise tens or hundreds of containers. Clearly, some sort of automation tool is required. Enter container orchestrators: Kubernetes, Docker Swarm, Apache’s Mezos, Hashicorp’s Nomad, and AWS’s own container orchestration tool: AWS Elastic Container Service (ECS).

AWS ECS

Container orchestrators like ECS manage the entire lifecycle of a container, including starting, rescheduling, load balancing, etc. Let’s understand how this works.

The first step would be to create an ECS cluster. This will include all the services needed to manage individual containers in the cluster. In other words, the ECS cluster acts as the control plane for your VMs that are running containers. These VMs are nothing but EC2 instances, which will host the containers and be managed by us. The EC2 instances will have the container runtime and ECS agents installed. This will enable the ECS processes to communicate with individual EC2 instances and manage them. I.e. you can delegate management of your containers so you don’t have to contend with the hassle of repetitive, manual container management tasks.

However, you do still have to manage the VMs, i.e. the EC2 instances. Viz: create the EC2 instances, join them to the ECS cluster, and ensure you have sufficient instances and resources to schedule the next container and manage the server OS, as well as the container runtime. On the positive side, you have complete access to your infrastructure. If you want AWS to manage the hosting infrastructure as well, there is a solution:

AWS Fargate

AWS Fargate is basically a serverless compute engine designed for containers. Since it is fully managed by Amazon you’re spared the task of provisioning or managing servers. Amazon assumes the responsibility of managing your server infrastructure. And of course, you only pay for the resources you need to run your containers.

Think of Fargate as an alternative to EC2, but instead of provisioning EC2 instances and connecting them to your ECS cluster, you delegate this to Fargate, which will spin up the VM to run your container, after analyzing how what resources—CPU, RAM, Storage—it needs to be deployed and run. All of this happens automatically. All you need to do is hand over your new container/s to Fargate—using the interface. The advantages for you are many. To begin with, you don’t have to bother about having sufficient EC2 instances or resources to schedule a new container. You don’t have to provision any infrastructure before you deploy your containers and you only use as much infrastructure and resources as your containers actually use—which means you pay only for what you use. In comparison when using EC2, you pay for the whole server even if you’re only running a few containers or none. With AWS taking on the responsibility of managing your infrastructure, you’re free to focus on the management of your actual application itself. On the flip side, if you need access to the actual infrastructure running your containers, EC2 is a better fit.

One big advantage of running your container application on AWS is that you have access to multiple supplementary services from AWS. For instance, CloudWatch for monitoring, Elastic Load Balancer (ELB) for load balancing, IAM for permissions, etc.

You will also have the storage (Etcd) replicated so you don’t lose data like cluster configuration. AWS also handles data backup. In a nutshell, with EKS the Kubernetes master nodes are not your worry any longer. All you need now are the worker nodes, the infrastructure that actually runs your containers. Here, again, you follow the same process as in ECS. You create EC2 instances—the compute fleet—of virtual servers and connect them to the EKS. This gives you a complete Kubernetes cluster and you can connect to the cluster using the Kubectl command and begin deploying containers inside the cluster.

Communication in ECS happened through the ECS agent installed on the EC2 instances. This allows the control plane to communicate with individual nodes. In the Kubernetes world, the worker nodes and master nodes communicate using Kubernetes processes. These are not specific to AWS.

As far as managing your EC2 instances is concerned, in EKS too you have to manage the OS yourself and the processes running on your EC2 instances. But you can make it easier on yourself by choosing semi-managed EC2 instances. This allows you to group your worker nodes logically. The semi-managed option creates and deletes EC2 instances, which makes it easier to manage. Your node groups automatically have all the necessary processes installed on them, so you don’t have to install container runtime Kubernetes worker processes to make worker nodes.

You still have to manage other tasks—autoscaling, for instance. Autoscaling is not configured out of the box. You need to configure settings in Kubernetes and the AWS side of things to make autoscaling possible. And you have to handle the creation of new EC2 instances yourself. If you don’t want this hassle too, simply use Fargate. You can also have your containers running on EC2 instances and Fargate simultaneously for the same EKS cluster.

Amazon EKS

But what if you still want to leverage the AWS ecosystem and you’re using or want to use Kubernetes? After all, it is the most popular container orchestration tool right now. Amazon has a solution for that as well: Amazon EKS (Elastic Kubernetes Service). As mentioned, if you’re already using Kubernetes, i.e. your project is deployed on a Kubernetes cluster and you want to put it on AWS infrastructure, you can still retain your Kubernetes tool, instead of using a proprietary tool like ECS. This way if you decide to migrate to some other platform at some future date, you can do this easily with EKS; because, although AWS is managing your Kubernetes cluster, Kubernetes itself is not proprietary to AWS; it is an independent tool, so you can use it anywhere—on another cloud platform, or even on-premises if you have your own infrastructure. There is no doubt that Kubernetes is a very popular orchestration tool, and you also have access to a big community and multiple tools and plugins being developed in the Kubernetes world. However, if you’re using other AWS tools and services in your Kubernetes cluster, you will have to replace them with other options, as they are specific to Amazon.

EKS working on AWS infrastructure is similar to how ECS works. So you create a cluster—this represents the control plane and will be the master nodes in your EKS cluster. AWS will provision Kubernetes master nodes in the background, and these master nodes already have all the necessary Kubernetes master services installed—all of this from provisioning to management is handled by AWS. Another thing that AWS does for you is to automatically replicate the master nodes across the Availability Zones (AZ) in the region you’ve selected. I.e. if there are three AZs in your region, you will have automatic replication of your master nodes in all three AZs.

Conclusion

AWS offers a choice of container orchestration tools. If you’re locked into AWS, you have ECS, whereas if you prefer to keep your options open, or are already using Kubernetes, you can continue to do so, while leveraging AWS infrastructure. For users who don’t want any hassle of managing the underlying infrastructure, AWS offers Fargate—a serverless compute engine designed for containers. And it works with both ECS and Kubernetes. Fargate saves you the bother of managing your server infrastructure so you can focus on your app development instead. And of course, you have a cost benefit as you only pay for the infrastructure and resources you actually use.

Serverless or Containers, Which is the Better Option?

Mon, 14 Nov 2022 05:54:53 +0000

Both serverless and containers are new disruptive technologies designed to allow developers to build applications with more flexibility and at lower costs than the traditional method of building applications that are hosted on servers or virtual machines (VM). But which one is the better option? As we will see there are several differences between the two and the choice of which to use depends on the needs of the application, traffic predictability, etc.

Serverless Computing

The first thing to understand is that although the term is ‘serverless’, it doesn’t mean there are no servers. As of now, at least, every workload needs a server. But in the case of serverless, the servers have been abstracted away by the Cloud Service Provider (CSP) so the user doesn’t have to bother about managing the underlying infrastructure.

Management hassle

Since the CSP assumes total responsibility for the actual servers, with serverless computing you’re saved such tasks as provisioning, monitoring, patching, scaling, etc.

Since you do not control the underlying environment, it also means that you cannot install software, like Web Servers or Appservers

Automatic scaling. You can, however, install code libraries—for instance, if you have a Lambda function written in Python and has some dependencies on certain libraries, you can package the libraries with Lambda.

Another distinctive feature of serverless computing is that it automatically scales up and down in tune with actual traffic. This means you do not have to define an autoscaling group or schema. The CSP makes it happen for you.

Pay for what you use

Not only do you not have to bother about the infrastructure, but you’re also not billed for non-usage, i.e. if your workload remains idle, you’re not billed at all.

Highly available

The final differentiator that has made serverless computing so popular is its high availability.

Memory

Serverless offers no—or very small storage space, and it is temporary

Traffic

When traffic cannot be easily predicted, serverless is the better bet, as it scales up automatically and you only pay for what you use.

Amazon offers several serverless services, the most commonly used ones are Amazon DynamoDB, Amazon API Gateway, AWS Step Function, Amazon Simple Queue Service, and the crown jewel of AWS’s serverless offerings: AWS Lambda.

Key benefits of serverless computing

Containers

As its name implies, Containers are a kind of virtual box in which the code and all its dependencies, i.e. configuration, code, runtime engine, etc., are packed away. The most popular container form is called Docker. The advantage here is that since everything is contained in the package—i.e. the Docker container—you can run the application smoothly in any computing environment. Of course, the containers still have to be managed, i.e. aspects like deployment, networking, scaling, health monitoring, etc. must be managed. Losing a node or process, for example, can disrupt your service and cause downtime. It’s not possible to manually manage multiple containers; a problem that is solved by container orchestrators like Kubernetes, Amazon EKS, Amazon ECS, and Docker Swarm.

You can take a closer look at the differences between ECS, EKS, and Fargate, here.

What does a container contain?

Container management

Unlike serverless, where the CSP handles management tasks, with containers, since the user has full control of the underlying infrastructure—VM, OS, etc., it follows that any management and orchestration that needs to be done falls to the user.

It also means that you are free to install almost any software that you need—and perhaps the most attractive feature about containers is that you can use pre-packaged images that are already available. For instance, say you are developing an app in Java; you will need the Java software installed, some testing tools, etc. A container with all of this is already made available to you. You just need to pick it up and run it.

Compute power

Serverless lets you pick the compute power you need—from 128 MB to 3 Gigabytes, with a runtime time limit ranging from one second to 15 minutes.

Where containers are concerned, adjustment of your VM parameters needs to be done by you. Once your container is up and running, it can be tricky to change an EC2 instance type. Ideally, you need to plan for this beforehand.

Memory

Containers come with hard disks attached to the nodes

Use Cases

Serverless is well suited for event-driven architectures; it can be natively integrated with other cloud services, making it easy to trigger your Lambda instance when it is needed.

Containers excel when you need to run specific software; say you need to run a webserver or an app server, it is easy to install it in a container and run it. This is not possible serverless, simply because you have no access to the underlying infrastructure.

Traffic

Serverless are the definitive winner in use cases that see unpredictable traffic.

Conversely, containers are better suited to use cases where the traffic is predictable because you’re paying for the underlying instances, whether you use it or not. When containers scale the entire VM scales and when traffic increases beyond its capacity, it spins up another Kubernetes node—i.e. another EC2 instance—and even if this node is at just 50% utilization, you have to pay for the entire EC2, i.e. you’re paying for idle resources.

Cost Difference

The difference in cost incurred in Serverless and Containers can be best explained with a couple of use cases

Scenario 1

In the first case, let’s assume your traffic volume is three million a month, each execution consumes 512 MB of memory and takes 300ms. Let’s also assume that the traffic is completely unpredictable. Using the AWS-provided lambda calculator (you can try it out here), you will see that it costs just USD eight per month.

In this same use case the container configuration will cost as follows: US$144/month for the control plane; $14 for a small EC2 (t3), which will function as our worker node. This comes to around $160/month, and the cost will increase if there is a spike in traffic, which we have assumed is wildly unpredictable.

Scenario 2

In the second use case, let’s put the traffic volume at 90 million per month; memory @512 MB/execution, and 250 milliseconds per execution. Finally, let us assume that the traffic is pretty predictable.

The cost here comes to $206 for Lambda and with the same parameters the container cost will be as follows: $144/month for the control plane; $29/month for a t3 medium worker node, making it $173/month.

The above scenarios make one thing clear: there is no blanket winner, where cost is concerned. If the traffic is predictable, using containers allows you to select the right VM and CPU, making containers a better option in such cases.

Conclusion

In a nutshell, serverless services, like AWS Lambda allow you to run code, which meets high traffic demands at any time, without provisioning or managing servers. And, of course, you don’t pay for idle resources. However, if you need more control over the underlying environment, say access to a web server, and the traffic is quite predictable, containers are the way to go.

Why and how to migrate MS SQL server DB schema?

Thu, 10 Nov 2022 06:09:05 +0000

Migrating a database from on-premise to cloud can be a daunting task, but it can also be an opportunity to save costs and improve efficiency. In this blog, we’ll share our experience of how we migrated our client’s MSSQL Server database to an open source PostgreSQL database hosted on AWS cloud, and the benefits it brought.

The time estimations for the migration were based upon the complexity of individual databases. For highly complex databases, which had a huge number of entities like procedures, functions, triggers, etc., the average migration time would be around 10-12 weeks based on our past experiences. Similarly, for simpler databases with fewer numbers, the migration time is around 5-6 weeks.

After migrating the database to the cloud and using AWS services like AWS Aurora, we were able to save the client almost 400K$ to 500k$ in both operational and licensing costs. This was a significant reduction in their expenses, which allowed them to invest more in other areas of their business.

This database migration was developed in a separate environment, not in production. Once the complete database was tested and analyzed, the database was moved to production by replacing the original database, resulting in close to zero downtime in the application.

During the migration, we also included an additional feature to test the logical entities in the database using unit testing. By writing unit tests and creating a dynamic HTML code coverage report, we were able to analyze and pinpoint exact blocks of code that were not unit tested.

The whole operation of unit testing and code coverage calculation was automated using Jenkins pipeline, and testing was done in containerized databases. This resulted in an extremely fast and efficient testing methodology, and we were able to test around 300 entities within a span of 10 minutes.

Thanks to this testing feature, the client could test their complete database within 10 minutes and increase the standardization and confidence during feature deployments, upgrades, and analysis of their database.

In conclusion, migrating to open source databases can bring significant cost savings and improved efficiency. By utilizing AWS services and implementing automated testing methodologies, we were able to migrate our client’s database to PostgreSQL and save them a significant amount of money.

If you have any other questions, feel free to ask us.

Ten Reasons to Handover Your Cloud Management to an MSP

Wed, 09 Nov 2022 06:31:44 +0000

Moving to AWS cloud is clearly beneficial on so many levels, higher scalability, better security, less stress about monitoring and managing your infrastructure, and so much more. But to enjoy all these advantages you need to know how to make the most of the cloud: which services you need now to optimize them, for cost and performance, how to secure your workloads, ensure resiliency, etc. The answer: AWS Managed Service Providers (MSPs). Amazon’s Managed Services Providers can help you stay competitive, solve business issues and drive key outcomes, through their expert guidance. From migrating to AWS to optimizing your workloads, your MSP works with you at every stage of your cloud journey.

Here are the top ten ways your MSP can help you get the most from your AWS cloud.

1. Savings

The upfront cost of acquiring and managing IT infrastructure in-house is one of the biggest reasons to move to the cloud, as it provides excellent economies of scale, eliminates the need to provision infrastructure, and eases administrative tasks. But it all still needs to be picked, configured, and deployed. Which calls not just for expert knowledge but several specialized tools too. Availing of the services of an MSP is a huge help in doing all of this. To begin with, MSPs already have the tools and resources you need to set up and maintain your infrastructure. Since you only pay for what you use and for as long as you use it, you are effectively reducing your costs. No to mention, you’re conserving capital by moving expenditure to Opex. So your budgets can be spent on more urgent business needs. This is a significant cost saving for SMEs.

2. Infrastructure Optimization

Building and maintaining network infrastructure involve many components and processes; and the responsibility of provisioning, monitoring, and managing all of it falls to you. When you partner with an AWS MSP, a substantial portion of this responsibility is taken off, and so are the expenses.

Migrating legacy workloads to the cloud can be a complicated affair: do you lift-and-shift, refactor, rehost, or replace your apps? MSPs can help you decide the best-fit strategy for you as well as simplify and streamline the entire process, helping you meet your business goals with a more integrated business strategy.

MSPs typically provide 24×7 management services to ensure your infrastructure is secure and available. This reduces expenses, eliminated (or reduces) downtime, removes the responsibility of managing your infrastructure, and allows more leeway to diversify and focus on your core business needs.

3. Streamlined Processes

Your processes and tasks are interlinked with the rest of your infrastructure to some degree. This means that every action you take to manage any piece of your infrastructure will have some knock-on effect on all the processes it is linked to. So before you go in for any change—an update or upgrade, for instance, you need to get on top of this complexity. Your MSP can take over the responsibility of monitoring your processes and handling changes. The MSP has the infrastructure and the expertise to handle any problem that may crop up, be it in your day-to-day routines or during new releases.

4. Data Management & Disaster Recovery (DR)

If you operate an on-premises IT Infrastructure, you know firsthand the stress of possible loss of data or server failure. This is a nightmare that haunts all businesses, even those operating their infrastructures in the cloud. Any data loss can trigger unplanned downtime, compromising day-to-day operations, negatively impacting growth, and compromising brand equity. Ensuring your data recovery processes and tools are operational 24×7 is a full-time task. But with an experienced MSP on the job, you can avoid major catastrophes, and even a major situation, should it occur, can be handled with minimal downtime.

5. Automatic Upgrades

The IT industry offers fertile grounds for new technology, with new applications, tools, and trends being released almost by the hour. Some might need just a tweak or two, but a fair number of them require major upgrades or new solution integrations. Plus the need to keep up with changing business needs and regulatory compliances often forces organizations to adopt new technologies or tools. Having a capable MSP at the helm allows organizations to navigate these changes with as little disruption as possible, and often saves you the cost of employing new talent or having to up-skill your workforce, which is a neat segue to our next point.

6. Strengthen your team without new hires or up-skilling

Along with new technology upgrades, comes the need to also upgrade your workforce’s skill sets, and this can be time-consuming and expensive. Hiring workers with the cutting-edge skills you will need won’t come cheap, and remember, those skills might soon need upgrading too. The other option is to re-skill your workforce; get them to earn the required certifications, etc. This is also a time-intensive affair, plus they won’t have any experience with the new skills and will, in effect, learn on the job.

You can avoid all this heartache and expense simply by engaging a qualified MSP. This gives you direct access to staff with the certifications and experience to match. And the responsibility for keeping those skills current rests with the MSP, not your HR.

7. Responsiveness

Although it pays to be vigilant, there are a lot of inconsequential anomalies that your security system will flag. Separating the noise from the truly dangerous can be a fatiguing business, but any miss could well be a catastrophe. A reliable MSP saves you this tedium. There are many cutting-edge tools—including several ML-powered ones—that flag potentially dangerous threats, and the right MSP ought to have access and familiarity with those tools, and will be alert to any threat.

8. 24×7 Management

In a world where downtime can severely—sometimes, permanently—damage businesses and cause major losses, you need constant supervision to ensure business continuity. MSPs bring a high level of security scrutiny that will pre-empt problems and catch anomalies before they cause security breaches and/or compliance issues.

A properly qualified and experienced MSP will ensure that your infrastructure is constantly monitored and requirements are taken care of as and when they arise, with the right solution—such as updates, security patches, or audits.

Putting your infrastructure under close and constant scrutiny also ensures that you meet any required compliances.

9. Resources freed to focus on core business

With a qualified and experienced MSP keeping a 24×7 watch on your workloads and data in the cloud, your staff is free from routine tasks like monitoring, updates, and maintenance. This effectively releases your organization’s resources from infrastructure- and process-related responsibilities, allowing them to focus on core business activities, such as product/services innovation, sales, and growth.

10. Centralized Data

When you migrate to the cloud, your operational data and information are moved to a centralized system. And this brings several benefits:

Higher data integrity

With your data consolidated in one central location, you have one single source of truth—the assurance of reliability and accuracy wherever the data is used, throughout the organization. You also enjoy better data portability.

Lower data redundancy

One of the big downsides of autonomous databases is that it leads to data duplication. Centralizing the data reduces redundancy, which helps reduce errors. It means everyone will be working with the same data sets.

Reduced cost

Since your data is in one centralized location, you’re also saved the need to pay for multiple locations, this also includes the cost of administration, and multiple license fees (for different software). It is also a time saver, since you can collate and analyze data easily, as you don’t have to consult multiple data sources.

Better insights

With cleaner data comes the opportunity to mine it for real actionable insights, allowing for better business planning and reporting.

As we’ve seen there are many definitive advantages to engaging with the right MSP. As part of AWS’s Partner Network, we have in-house expertise and cross-industry knowledge to help you meet your cloud goals, quickly and cost-effectively.

Need help with your cloud? Connect with Applied AI Consulting (AAIC) today.

How much will your cloud cost?

Mon, 07 Nov 2022 06:44:50 +0000

Savings are one of the primary reasons to move to the cloud. However many a time, organizations are confronted with bills that are larger than earlier on-premises expenses. Let’s understand why this happens and how you can save yourself the hand wringing by calculating your migration costs before you transition to the cloud.

Computing the cost of cloud migration is, unfortunately, not straightforward; in addition to comparing differences between on-premises and cloud prices, you have to consider a variety of other costs, such as data storage, refactoring, security, etc. It is easy to overlook expenses like employing new staff or deploying new services. Here’s a checklist of costs you must factor in to ensure your cloud migration is financially beneficial.

Calculate your current on-premises costs

This is the very first step to undertake before you begin to move to the cloud. Your calculations should include the cost of existing hardware and software, and compare it to cloud-based options. This is not as easy as it looks, as on-premises infrastructure involves Capex expenses while cloud resources don’t, as they follow an Opex model. IaaS virtual hardware and SaaS applications only incur costs at actual. To overcome this you have to convert your on-premises Capex costs to Opex. One way is to divide your resources cost by the time you usually spend using them. while this isn’t an exact conversion—as it doesn’t account for costs that might augment the server’s life, like additional memory, or new hard disks—it nevertheless gives you a broad baseline to compare with equivalent costs in the cloud.

There are also some resources that you don’t need to pay for, like network switches, UPS units, network-attached storage, etc. You also won’t attract expenses, like electricity, physical site security, post migration.

Refactoring

It is common to see organizations simply transfer their applications and data (housed in on-premises storage) to public cloud (compute and storage services)

Here no refactoring costs need to be calculated, making migration costs simple to compute. However, this method—known as life-and-shift—may not be in your best interests and undermines some of the basic reasons you chose to move to the cloud. For instance, you may have large monolithic applications that can benefit from migration to containers and serverless computing.

Of course, modifications such as converting a monolithic application to microservices is a cost factor as it requires a set of cloud services, many of which need the expertise to administer effectively.

Cloud costs

Once you have calculated your on-premises IT cost, you need to calculate the cost of your planned cloud and compare both.

Calculating cloud costs involves many variables, there are different vendors and their pricing schedules to consider. Many also depend on regions and how many resources you consume. High volumes of cloud storage cost less than just a few gigabytes. The cost of reserving certain cloud services in advance is also less than simply consuming them as you go along. To make this task simpler, you can use calculator tools designed for this express purpose. AWS offers its AWS pricing calculator. Azure and Google Cloud too provide their own pricing calculators. Remember these calculators are native to their specific clouds. If you prefer a third-party option, try Apptio Cloudability or CloudCheckr; although these are more cost-optimization tools and are better at identifying the most economic option for your needs, they can’t predict costs as accurately as the cloud vendors’ own calculators.

Cost of auxiliary services

There is an array of add-on services that you’ll probably leverage once you move to the cloud, and these need to be calculated too.

Auxiliary services include services like CDNs (Content Delivery Network) add-on services to enhance cloud workload security, and improve performance. Obviously, the more of these services you use the higher your cloud bill.

Hidden costs

There are various costs that can be easy to overlook, but must be anticipated. Don’t overlook costs like the ones below:

Cost of data migrations: If you have very large volume/s of data to move, you may end up using services like AWS Snowmobile, which will add up your costs.

Skills: If your existing IT team doesn’t have the requisite skills you will need to engage the services of a professional IT services business, which will add to your total cost.

Consultants: You may decide to hire a consulting company that specializes in handling cloud migrations, this is, for many, the smart choice, but it does augment cost.

Backup: don’t forget to calculate the cost of backup storage in the cloud—this cloud be on-premises or another cloud or a different region

Cloud management

A lot of your cloud management and administration can be automated. Tools like AWS Step Functions and Auto Scaling can automate workflows, or you may want to use Infrastructure as Code (IaC) to automate provisioning and deployment. Setting up these tools will add to your costs, as well.

Orchestration costs

Depending on the type and scale of your workloads, you may want to host them in containers, which calls for an orchestration platform, like Kubernetes. If you aren’t using Kubernetes currently, you will have to first set it up; also the cost of Kubernetes on-premises and in the cloud is different. Or you may choose to avail of Managed Kubernetes services, whatever you decide you should study the costs closely.

Infrastructure write-off

Unless you plan to go hybrid, there will be infrastructure in your on-premises environment that you just won’t need in the cloud. This will have to be decommissioned, and writing off infrastructures that have a useful lifespan remaining is an expense too. The exact cost of this write-off depends on how much life the hardware has and if you can repurpose it or sell all or some of it off.

For cost optimization Book a free 30 minutes consultation call

AWS Well-Architected Framework: optimizing productivity, security, and cost of your AWS cloud

Mon, 07 Nov 2022 06:37:49 +0000

AWS gives you all the tools and services you need to build your cloud. The challenge lies in knowing which ones you should use to implement and operate your workloads. One common analogy is that of trying to build a house by walking

into a hardware store and purchasing all the items you need. The first problem is understanding which items you need, the second is understanding how they go together. AWS Well-Architected Framework helps you do all that. Plus, it helps you

understand if your decisions are the correct ones, in other words, will they deliver in terms of performance, security and

cost? In short, the AWS Well-Architected Framework provides a structure to compare your use of AWS with its own best practices.

The Well-Architected Framework is built around six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. It enables customers and partners to evaluate architectures and implement scalable designs.

The Framework includes the AWS Well-Architected Tool, which is available free of cost in the AWS Management Console. It provides a mechanism for evaluating workloads, identifying high-risk issues, and recording improvements.

Through its partner program, AWS also provides access to a network of partners, so you can engage with a partner in your area who can help analyze and review your applications.

Benefits at a Glance

Workloads that follow the AWS Well-Architected framework have the ability to: Build and deploy faster

Mitigate technology risks before they happen Make better informed decisions

Implement AWS-recommended best practice

Pillars

AWS Well-Architected Framework comprises six basic pillars:

Operational Excellence

This pillar focuses on running and monitoring systems, and continuously improving processes and procedures. This includes automating changes, response to events, and setting standards to manage daily operations.

Security

The security pillar is primarily concerned with protecting information and systems. It include information pertaining to confidentiality and integrity of data, managing user permissions, and establishing controls to detect security events.

Reliability

Workloads need to perform as per their intended functions and must be able to recover quickly from failure. Key topics of the reliability pillar include distributed system design, recovery planning, and adaptability to changing requirements.

Performance Efficiency

This pillar focuses on the structure and streamlining of IT computing resources, it includes topics such as selecting resource types and sizes to optimize workloads, to monitor performance, and maintain efficiency as business needs evolve.

Cost Optimization

Key topics in the Cost Optimization pillar include understanding of spendings over time, managing fund allocation, selecting the right type and quantity of resources, and scaling up or down to meet business needs cost-effectively

Sustainability

There are environmental consequences to running cloud workloads, and the sustainability pillar focuses on how to minimize this environmental impact. It includes information about the shared responsibility model for sustainability,

understanding the impact of resources and how to optimize utilization of required resources so as to reduce downstream

impact.

Each of these pillars provides information covering design principles—general and specific. The pillars also provide the information needed to review your AWS infrastructure.

Well-Architected Framework Lenses

Lenses offer a way to consistently compare your architectures against best practices and identify areas for improvement. The AWS Well-Architected Framework Lenses are applied whenever a workload is defined. Workloads may have one or more of the Well-Architected Lenses applied. Each with its own set of questions, best practices, notes, and improvement recommendations.

AWS Well-Architected Framework provides the following lenses:

Serverless Lens

The serverless lens helps you apply best practices when building serverless application workloads on AWS. It covers RESTful microservices, mobile app backends, stream processing, and web applications.

The SaaS Lens

This lens focuses on design, deployment, and architecting of Software as a Service (SaaS) workloads in the AWS Cloud. Use it to apply best practices when building SaaS workloads on AWS.

The FTR Lens

This lens is aims at enabling independent software vendors (ISVs) prepare for a Foundational Technical Review (FTR) in the AWS Partner Network (APN).

It provides questions for ISVs to conduct a workload self-assessment before requesting a review.

Users can also create and use their own custom lenses, or use custom lenses that have been shared with them.

Even if a lens is removed from a workload, the data associated with the lens remains, and is restored if the lens is added back to the workload.

Well-Architected Review

It is recommended that you conduct Well-Architected Reviews periodically to encourage a consistent and structured approach. This will ensure that you get the best return on investment for your efforts.

AWS Partners, like AAIC are qualified by AWS to perform reviews. The process consists of exploring functional and non- functional topics through Q&As, with the objective of qualifying the current state of the workload’s architecture and

implementation.

If you want to be sure you’re getting the most from your AWS cloud, there’s no better way that to conduct a Well- Architected review. Contact AAIC today to get started.

How to Choose the Best Cloud Provider and AWS Partner

Thu, 03 Nov 2022 06:56:47 +0000

If you’re reading this, you’ve probably already zeroed in on the cloud as the home for your IT infrastructure. If you’ve chosen Amazon Web Services (AWS), it’s not surprising. AWS is and has been the leader in cloud services since the company pioneered the very concept of it, way back in 2006. In 2021, Gartner for the 11th successive year, again named AWS as the leader in cloud services. So if you’ve picked Amazon Web Services to be your cloud provider, you’ve probably made the right decision.

But even if you’ve already done your due diligence, here’s a short checklist to ensure you’re selecting the right cloud service provider for your needs.

Certifications & Standards

Certifications are the definitive way to demonstrate adherence to industry best practices and standards. Picking providers who comply with accepted standards and quality frameworks should be the first step in shortlisting potential providers. Look for accredited certifications like ISO 27001, as well as general ones for structured processes, data management, security, et. al. Also find out how the provider plans to maintain adherence to these standards in the future.

Technologies and Services

Can the provider’s cloud architectures, standards, and services support your workloads? How much customization will you have to do to align your workloads to their platform norms? These are important questions to ask, because many a time, you may need third-party support to fill the skill gaps. In these cases, does the platform have a partner program from which they can recommend partners who have the necessary expertise and experience?

It is also important to know the provider’s plans for service development and to understand how well their plans to innovate and grow align with your own long-term needs. Important aspects to consider here include the portfolio of services on offer and commitment to particular technologies and vendors

Data

Ensure your provider can support regulatory data privacy rules. If you have specific requirements, pick providers that provide control over locations where your data is stored and managed. AWS is transparent about its data center locations but it is your responsibility to assess this factor including the provider’s ability to protect data in transit, i.e. moving to, within, and from the cloud. Understand the provider’s processes regarding data loss and breaches and make sure they meet your own legal/regulatory obligations.

Service Partnerships

Service providers usually have many vendors, and their relationships with their vendors is another important factor to assess. Consider factors like accreditation levels, technical abilities, staff certifications, etc. also understand how well the services offered integrate with complementing services, for instance: a SaaS CRM should integrate with finance and marketing.

Factors to help clarify risk

Contracts and SLAs

Cloud agreements are complex and you must ensure there is full transparency. SLAs in particular are often subverted through complicated, and ambiguous language. Check for out-of-the-ordinary terms and conditions. If you have any out-of-the-box terms, be sure that the provider regardless of their willingness to support them can also exhibit capability.

Some key factors, regarding contracts, to consider, include

Service Delivery:

Service definition

Roles & responsibilities

Service management

Service availability

Disaster Recovery & Continuity

Business Terms

Insurance

Fees and commercials

Publicity

Operations review

Data Assurance

Data management & security

Data ownership and usage rights

Data conversion

Legal

Indemnification

Intellectual property

Liability

Warranties

If you need more help in understanding any details regarding any of the above, get in touch with our team at the earliest.

Reliability & Performance

You can measure the reliability of a service provider in a number of ways:

Compare their previous year’s performance vis-a-vis their SLAs; check downtime experiences; ensure there are monitoring and reporting tools in place and that the provider has a proven and documented process of dealing with unplanned downtime, and communication such incidents with customers.

Check disaster recovery provisions, and the provider’s ability to meet your data management expectation in terms of recovery time objectives. If the costs pertaining to recovers are not fully covered by the provider, you can consider additional risk insurance.

Vendor Lock-in and Exit Planning

Vendor lock-in is a process whereby the service provider tries to keep you from easily transitioning to a competitor. These situations are usually the outcome of using proprietary technologies (if your applications need to be re-architected to run on a provider’s platform, for instance), inefficient processes or contract terms, among other factors. Such factors impact the portability of your apps and workloads to other providers. Common examples include

Proprietary application architecture/management tools

Customized geography

Proprietary cloud APIs

Customized web services, like databases

Unstandardized data formats

To protect yourself from vendor lock-in situations, you can do the following:

pick value-added services that have comparative alternatives

periodically review these alternative options

Watch out for modifications in configurations, policies, technologies, etc. that might be introduced retroactively or a part of ongoing review processes

Exit planning

It might seem contrary to plan an exit before a relationship has even begun, but think of it as a prenup to protect yourself in the event of a breakdown in the relationship. Migrating from one CSP to another is never smooth so make sure you know the process before signing on. One of the factors you must check is data access: i.e. its state, and how long the provider will have it.

Company Profile

Assessing the financial health of your potential provider is just as critical as checking their technical and operational capabilities. There is no point in signing on for the most compatible or economical cloud service if the provider’s business itself is not steady. The provider should have a robust tranck record of stability, with a healthy financial position, and the capital to continue operations in the long term. From a legal-financial standpoint, check if the CSP has or has had any legal issues, and how they might respond to litigation; check about planned mergers and acquisitions.

It’s a good idea to look at the CSP’s social media pages, e.g. LinkedIn, it can be quite revealing.

Selecting the Best AWS Partner

In the event that you’re due diligence validates your choice of AWS as your CSP, the next step is to pick the right AWS Partner.

Remember, although AWS is known to provide a highly reliable, scalable, and cost-effective infrastructure, with innovation at its core, its management doesn’t include management of your particular cloud.

Moving to AWS calls for specialized experience to take advantage of its vast menu of web services. This is why it is so important to choose the right AWS partner. To enable this AWS has instituted the AWS Partner Network—a collection of certified AWS experts who can leverage AWS technology and services to meet your business objectives. Here are the prime factors you should consider when choosing an AWS Partner for your business.

Why pick an AWS partner.

To answer this question, you need to ask yourself a few questions too. Such as

What are my goals—short- and long-term?
What do i hope to achieve?
What are my priorities
What internal AWS experience can I count on?
How involved do I want to be in managing my cloud?

Depending on your answers, you can choose…

the D-I-Y Approach: Using your own in-house resources, including certified AWS engineers

The Hybrid Approach: A bit of both often gives you the best of both worlds, plus you can build DevOps practices, improve your own team’s skills and push innovation internally

Total outsourcing: This is a good idea if your organization is new to AWS and you need to move quickly. This approach assures the best resources and professional service.

Applied AI Consulting: AWS Partner of Choice

As a member of the AWS Partner Network, AAIC can help you architect your infrastructure, migrate, and manage your workloads & applications to AWS and optimize it for efficiency and cost on an ongoing basis. We offer a full menu of cloud services, including migration, DevOps, and automation.

What’s in it for you?

When you partner with AAIC, you get the full benefit of a professional, AWS team with years of experience in deploying AWS projects. This will help you generate massive savings in time and money, not to mention, preventing errors that can undermine your competitiveness.

AAIC can chart and guide you on a transformative AWS digital path that is designed according to your needs, with resources and talent to help you succeed on the AWS cloud in the shortest possible time frame.

What does AWS do?

Wed, 02 Nov 2022 07:05:07 +0000

AWS offers such a vast array of services that it can be a little daunting to comprehend the full ecosystem. There are more than 130 different services and they span the full gamut of computing, including services for compute, analytics, storage, developer tools, security, and more. This article seeks to show you the structure behind the services. Rather than explain what each service is and what it does, we’ll understand the services by referencing them in the context of a typical architecture.

Ready? Let’s dive right in.

What does AWS do?

At its most fundamental level, AWS is a company that owns and operates data centers all over the world. Let’s understand the structure of these data centers.

At the global level are the AWS Regions. There are 20 such regions (with five more coming up soon) and they are located across the world, some in North America, a few in Europe, a few in Asia, etc. Each region comprises two or more Availability Zones (AZ), which are basically data centers. The name indicates their purpose. i.e. enabling availability. Typically, users have their workloads running in two or more AZs. This ensures that even if one goes down for any reason, your applications will still have high availability

AWS also has something called Edge Locations. There are present across 100+ cities. These are used to cache and deliver content directly to the user from the nearest location. This reduces latency considerably

Regions, AZs, and Services

To understand how these regions and services correspond to each other, think of the entire ecosystem as a hierarchy. At the top, you have the AWS Account. Your AWS Account allows you to deploy your infrastructure across AWS regions. At the account level, you have the billing services, and you also have IAM, which is AWS’s Identity and Access Management service. It allows you to create user/user groups, assign roles, permissions, etc. Because this is at the Account level, they would have access to all the Regions within your account (as per their permissions). Below this, you have the Regions. At this level, you can work with services like S3, and DynamoDB. When you create an S3 bucket you also have to select the region where your bucket will operate. The same thing applies to DynamoDb.

Next comes the AZ level. The scope of services here includes EC2—the core building block of AWS’s infrastructure—RDS databases, and Elastic Block Storage (EBS). Since the scope of these services is limited to the AZ level, you cannot have, for instance, an EC2 instance (which is basically a virtual machine running in another AZ at the same time.

AWS Services

As we saw, there are 130 different AWS services, and they can be broadly categorized as Compute (ECS, Autoscaling, Lambda Load Balancers) Data Storage, Data Analytics, Container Services, Network services, App Development tools, Management, and, of course, Security.

So, let’s see how these services come into play in a typical AWS cloud architecture.

For the purposes of this article, we are going to build a hypothetical application with the architecture it needs to run. The application will be a hypothetical Social Media website—a kind of Instagram or Facebook. Let’s call it iG2.com.

Finally, we will map the various AWS services to the different components in our architecture.

To begin with, we will deploy this application in our on-prem data center. To ensure security, we will need a private network. We will require an application server or web server. Users will access this application using our IP address initially. Now, over time, we will want to extend the application and add some business logic, and functions like log-in, etc. So we will put in a web server, as well.

Next, we need some kind of database, a relational database is the best bet, so we will put in a MySQL database. Now we have a 3-tier architecture and it’s doing fine. But as traffic grows, the webservers or application servers become a bottleneck, which means we need to scale. Typically 3-tier architectures are scaled horizontally, so we bring in multiple web servers, which means multiple IP addresses, then we put in a load balancer that will distribute traffic to the multiple back-end servers. Now we want our users to access the application through the domain name ig2.com so we need a DNS service using which the domain name is mapped to the load balancer.

As usage grows so does the data generated and the original database is unable to keep up with the diverse data and volume. Since we need a scalable database that can store various types of data without restriction, we pick a NoSQL database. To ensure the database doesn’t become a bottleneck, we put in a database cache engine. This way we can query the data without the application servers hitting the database, as the cache engine will serve all requests. Now since this is a social media website for sharing images and videos, countless pictures and videos will be uploaded every day, and the disc attached to the VM, having limitations, can’t scale on the fly. This means we need some unlimited storage solutions like a file storage solution or shared filesystem, preferably external, something like Google Drive. Now, some of the videos and images may contain vulgar or objectionable content, which needs to be filtered out as soon as the images/videos are uploaded, and stored elsewhere. So we need a content filter.

This application uses advertising as a revenue source, which means showing ads related to individual users’ content consumption patterns. What they like, what they click on, etc. So the application has to watch users’ actions, map likes and dislikes, and, based on the data, show relevant ads. This is known as clickstream analysis. As each click is analyzed we can get insights into trends. All this data has to be stored in a place from where you can run further data operations, which calls for a kind of Hadoop platform. You will also need a data warehouse. So let’s put that in, as well. Since this is the 21st century it is more than likely that your users are accessing the application using mobile devices. So now we need to have the videos in different formats suitable for viewing on different devices. This job can be done by a video converter. Whenever a user uploads a video, it is immediately converted into a mobile-friendly format.

Sometimes a video goes viral. When this happens the traffic to the external hard drive will be too much for it to handle. To ease the load we use a Content Delivery Network (CDN). These are simply caching devices located as close to the user group as possible. The content is then served up to the user from the nearest location, not necessarily the original. This improves performance and lowers latency. The next feature we need to add to the application is the ability to send notifications, for example, there’s a new post or message from a friend of one of the users. So we need to add a service for this functionality as well. This should also include the ability to message friends. We can use a queue service like RabbitMQ or IBM MQ for this. Finally, we have to monitor our architecture to know how the VMs, databases, storage, etc. are functioning. So we need a monitoring and dashboarding service. This then is how the architecture would look if it were deployed on-premises. Now if we were to deploy the same thing on AWS to take advantage of the many benefits that the cloud offers, like pay-as-you-go, elastic scaling, etc, here’s what it would look like.

Services in a typical on-premises architecture

The private network would be a Virtual Private Cloud (VPC) which AWS provides. This will be the base for our application and architecture.

The VM, in the AWS cloud, would be EC2 instances and their attached disks, Elastic Block Storage (EBS). You can incorporate auto-scaling for the EC2, so if traffic increases a new VM will automatically be spun up, i.e. horizontal scaling. If traffic decreases, they can reduce the VMs automatically. The relational database in AWS is RDS, and an AWS service known as DynamoDB is a NoSQL database. The database caching service is called ElastiCache and it comes with a Redis and Memory cached engine. The load balancer in AWS’s world is a service known as Elastic Load Balancer (ELB). This ELB takes care of distributing incoming requests among your back-end EC2 instances. The DNS service gets replaced with Amazon’s Route53.

For external storage—remember Google Drive?—it becomes Amazon’s Simple Storage Service (S3). S3 is an unlimited storage service, and it is accessible directly over the internet. For the content filter, we use AWS Rekognition, which filters our objectionable material before content reaches the S3 buckets. For seamless video conversion into a mobile-ready format, we use AWS’s Lambda service. Lambda is a serverless service. You just code the video conversion steps and it gets triggered and executed as a Lambda function whenever a video is uploaded. This is a serverless service so there is no server to manage and it scales automatically. Clickstream analysis is done by an AWS service called Kinesis. Kinesis collects the data and stores it in S3 for further analysis. The Hadoop platform becomes a managed service called EMR and it runs operations like aggregation, sorting, and running distributed jobs like SPARC and Flink.

Let’s not forget the DynamoDB data. You can run Extract Load Transform operations using EMR with a service known as Amazon Glue. All this data can be stored in the data warehouse, which is nothing but Amazon Redshift. Redshift can store massive data volumes…on a petabyte scale. Data analysis can be performed on this data using Amazon Quicksight or Athena (an SQL query engine). Quicksight also allows you to build graphs and charts to help deliver insight for your business decision.

The content delivery network is Amazon CloudFront, which stores your cached data in Edge locations in 100+ cities across the world. So users from a particular location are served content from the edge location nearest to them. For messaging and push notifications Amazon provides a Simple Notification Service, or you can send emails/bulk email using Simple Email Service (SES), or chat using Amazon’s Simple Queue Service (SQS). Monitoring your AWS cloud is done by an Amazon Service called CloudWatch. It also allows you to set alarms that get triggered by preset situations—say, your CPU utilization goes beyond a set percentage. In such instances, an alert is sent to the administrator, so they can take appropriate action.

Services in an AWS architecture

Now, what happens when you need to expose your application using API calls to allow a third-party application to integrate with your application? In Amazon, this is taken care of by API Gateways. All you need to do is write the code defining your API and deploy it in the API Gateway.

Security in AWS

The top level of security to manage all access is managed by AWS Identity and Access Management (IAM) It is the premier service to secure your AWS account and all your services. In addition to this, you can encrypt your data, which may be stored in EBS, S3, EMR, Redshift, and databases, using Amazon KMS (Key Management Service). This eliminates the need for a separate secure location to store your keys and perform encryptions. SSL certificates are managed by Amazon Certificate Manager ACM. Finally, you can deploy web application firewalls (WAF) in front of your API Gateway, and in CloudFront and your Load Balancers to prevent cross-site scripting, DDoS attacks, etc.

This is by no means the complete list of AWS Services, but these are the most commonly used services. If you’d like to know more about a specific service, let us know in the comments below.

Nine NLP Tools to help you get started with NLP today

Mon, 31 Oct 2022 07:27:13 +0000

Every day businesses generate a mountain of data; it is an inevitable result of almost every business interaction—emails, websites, blog posts, whitepapers, internal documentation, reports…the list is endless. But this is not clutter. There are valuable insights to be mined from this mountain of data. But analyzing this unstructured data near overwhelming for humans to sort through, and it’s just as hard for computers to understand. Enter NLP. A subset of artificial intelligence, NLP leverages linguistics and computer science to make human language intelligible to machines. By enabling computers to automatically analyze massive sets of data, NLP can sort through massive data sets to unearth meaningful information in just seconds. NLP can conduct various text analyses, including sentiment analysis, topic classification, and more.

Best of all, you don’t need to build an NLP application from scratch. There are several NLP tools that are available through SaaS models and open-source libraries that can be implemented easily.

If you’re looking to hit the ground running, SaaS tools are for you. These tools are ready-to-use, mostly cloud-based solutions that need little to no code to be implemented.

Pre-trained NLP models on SaaS platforms are ideal for those looking for a code-free solution; Professional developers—anyone with the ability to code, really—and those who want a flexible, low-code, option to simplify their work can leverage the APIs provided by SaaS platforms.

For those users looking for full customization of their NLP tools, there are open-source libraries. These are free, flexible, and allow major customization of your NLP tools.

As open-source libraries are aimed at developers, they can be fairly complex to grasp and users will need experience in machine learning to build open-source NLP tools.

Fortunately, most of these frameworks have support communities, so you can count on help when and if you need it. Ready to get started with using NLP, here are some powerful online tools that can help.

1. BERT

Bert—an acronym for Bidirectional Encoder Representations from Transformers—is an open-source machine learning framework, designed to help computers understand the meaning of ambiguous language in text. Bert works by using the text surrounding the problematic text to establish context. BERT framework was pre-trained using Wikipedia content and can be further fine-tuned with Q&A datasets.

BERT is based on a deep learning model called Transformers wherein each output element is connected to an input element, and the weightage between them is dynamically calculated based on their connection. Unlike traditional language models that only read text input sequentially, viz left-to-right or right-to-left, BERT is designed to read in both directions at once. This is described as bi-directionality.

Using bidirectionality, BERT is pre-trained on Masked Language Modeling and Next Sentence Prediction.

Masked Language Model (MLM) is used to predict a masked word based on the hidden word’s context. Next Sentence Prediction can predict whether two given sentences have a logical, sequential connection or whether their relationship is simply random.

2. IBM Watson

IBM Watson is an offering by IBM cloud. It comprises a suite of AI services, which are stored in the IBM Cloud. One of its key features, Natural Language Understanding, identification, and extraction of keywords, categories, emotions, entities, and more.

IBM Watson is versatile. It can be tailored to the needs of different industries, from HLO to Fintech, and has a trove of documents to help you get started.

3. Google Cloud Natural Language API

Google Cloud Natural Language API uses pre-trained models to run sentiment analysis, content classification, entity extraction, etc. Google Cloud also enables the building of bespoke ML models using AutoML Natural Language. Google Cloud Natural Language API is an element of Google’s Cloud infrastructure. it offers several benefits, including

Using Entity analysis to identify and label fields within documents, such as emails, chats, social media posts, and interactions, and run sentiment analysis to map customer expectations. Users can also use the Speech-to-text API to extract insights from audio content, while Vision API brings optical character recognition for scanned documents

To get you started on your NLP journey, Google Cloud’s Natural Language AI even gives new customers USD300 in free credits to use on Natural Language, Plus 5,000 units for analyzing unstructured text every month, which is free, i.e. not adjusted against your credits.

4. Amazon Comprehend

Amazon Web Services too offers an NLP service, known as Amazon Comprehend. It is integrated with the Amazon Web Services infrastructure. This API can be used for NLP tasks such as sentiment analysis, topic modeling, entity recognition, and more.

There’s also a separate variant for the healthcare industry: Amazon Comprehend Medical. It allows you to perform advanced analysis of medical data using Machine Learning.

5. SpaCy

SpaCy is one of the latest open-source Natural Language Processing services using Python libraries. It’s exceedingly fast and easy to use. SpaCy is supported by detailed documentation and can handle large data volumes. It also boasts a series of pre-trained NLP models that make your job even easier.

SpaCy makes it easy to select the best algorithm for each task; it simply serves up the best available option. Bu keeping its menu short and displaying the best-fit option, it saves you the task of work of going through a large menu of algorithms every time you want to run a certain task.

SpaCy’s library is a great option to prepare text for deep learning and extraction tasks. But only in English…as it is currently only available in English.

6. NLTK (Natural Language Toolkit)

NLTK is a Python library and another leading tool used to build NLP models. NLTK has built a large and active community, and it also offers several tutorials for language processing and sample datasets. Users can also avail of several resources provided with the toolkit, including a comprehensive Language Processing and Python handbook.

NLTK’s library does have a longer learning curve but it’s considered an amazing sandbox to obtain hands-on NLP experience. It has a modular structure and provides tools to conduct NLP tasks like tokenization, tagging, stemming, parsing, and classification, among others.

TextBlob

Like NLTK, TextBlog too is a Python library; in fact, it is built to work as an extension of NLTK. TextBlog lets you tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more. It has an intuitive, user-friendly interface that makes it simpler for users to perform the same NLP tasks (as NLTK), making it an excellent choice for beginners.

Stanford Core NLP

The prestigious Stanford University’s NLP community gets the credit for building and maintaining Stanford Core NLP. The tool is built using Java; which means users need to install JDK on their computers to use it. However, it provides APIs in most programming languages.

Stanford Core NLP toolkit enables users to perform a variety of NLP tasks, including tagging (parts of speech), tokenization, and named entity recognition, among others. It is built for scalability and speed, making it a good choice for complex tasks.

Gensim

Gensim is a specialized Python library. It is mostly used to handle topic modeling tasks, for which it uses algorithms like Latent Dirichlet Allocation (LDA). Gensim excels at recognizing text similarities, texts indexing, and navigating different documents. It is fast, scalable, and can handle large volumes of data with

Ten ways NLP is adding value to real-world business operations

Mon, 31 Oct 2022 07:11:22 +0000

Natural Language Processing (NLP) has become a real game changer for businesses—some more than others. But no matter what your business or sphere of work, there are many ways in which NLP can help ease your workload, improve accuracy, and save money. Here are ten real-life use cases for NLP in business.

1. Document (Text) Classification

Text is everywhere: emails, chats, website content, social media posts, blog posts…there’s a lot of it out there and it’s mostly unstructured, making it hard to extract any kind of value. Which is where NLP comes in. Text classification or text categorization is simply the process of segregating text into specific groups. NLP automates this process and assigns pre-defined tags to each based on the content of each document. This makes the documents easier to manage, sort, and mine for specific information. For instance, say you receive a mountain of emails every day. It’s all mixed up and you can’t risk deleting it en masse for fear of throwing out the baby with the bathwater. NLP can classify these documents in a flash, by separating them into various categories, based on the occurrence and frequency of certain keywords. Text that has keywords pertaining to more than one category can simply be categorized under both classes.

2. Sentiment Analysis

Every time we use emojis to register our approval (or dislike) of certain posts on social media or post reviews of a product or service we are engaging in expressing our sentiments. This is invaluable for brand/business owners, who use it to inform their business/brand strategies. But sentiment analysis can go far beyond this. For instance, it can be used to determine market/stock trends, based on the frequency of certain keywords.

Sentiment analysis can also be used by HR departments—using posts, feedback surveys, reviews, etc.—to gauge employee satisfaction with their job roles and strategize accordingly to enhance attrition.

3. Data Mining

Every day we generate a massive volume of data, most of it unstructured, but no less valuable. To unearth this intelligence, however, it is necessary to sift through tomes of text—next to impossible to manage manually, but a breeze for NLP. NLP solutions can automate the extraction of specific data related to a particular topic from any source, be it text documents, databases, or websites.

NLP can be used to train models to classify documents according to such variables as ‘date’, ‘person’, geography’, organization, or an ‘event’. The Fintech industry, for instance, uses NLP models to extract data from statements, reports, claims, and forms. This reduces the chances of error associated with manual entry, increases accuracy, and automatically sorts documents according to custom rules.

4. Chatbots

NLP can give chatbots conversations near human sentience

Who amongst us hasn’t thrown up their hands in exasperation after a frustrating session with a chat application? Conventional chatbots—even those personalized with names, and anthropomorphic features—are just not up to the task of conversing with humans. Enter NLP. Chatbots built using NLP solutions are now almost sentient. They can understand intent, converse in context and learn from previous conversations, making each interaction better than the one before. Bot models can be built and trained to perform specific conversations without needing to collect and tag extensive datasets for their training. The uses for chatbots, and voice assistants is seemingly limitless, from conducting interviews for HR to performing certain actions like Siri does. And since it is designed to take an automatic learning approach, it just gets better the more you use it.

5. Text Summarization

Typical process of automated text summarization

Information overload and Content Shock are very real. The continuous flood of information can be hard to process or use effectively. Summarization allows you to condense this voluminous information into convenient formats that are easy to digest. Summarization can be particularly helpful in the following cases:

Media Monitoring
RSS feeds
Website content—with a view to SEO.
Internal knowledge—typically stored as unstructured data
Financial research—earning reports, financial news, et al.
Contract analysis
Repurposing long-form content for social media marketing
Email—enabling us to skim emails quickly

Like to know how summarization can help you? Ask us in the comments below, or reach out to our NLP experts.

6. Custom search applications

Nobody (well, almost nobody) really knows for certain how Google’s algorithms might change in the future, but one thing is certain: NLP will play a big role in redefining how search engine marketing works. With NLP, search queries can better understand the intent behind the search…for instance homophones like accept and except; or homonyms like right (as in correct) and right, as in the opposite of left. The implication for SEO marketers is clear: content is doubly important now, as in content that is designed for human consumption rather than search bots.

For business organizations, search engines play an important role too. They help search emails, documents, etc. When searching for anything using any query, the search widget creates a list of documents matching the query. And it uses an index of documents/vocabulary to do this. Enterprise search engines that are built using NLP models will not face typical problems such as longer search queries or words that have dual meanings. A custom search engine is thus able to quickly and accurately find documents according to the searcher’s real intent.

7. Text similarity

Plagiarised content is a huge problem, not just in academia but in everyday writing as well—this article, for instance. Using NLP it is easy to build applications that flag documents that show a high degree of similarity—not merely by matching words, but by searching for semantical similarities, too.

8. Language translation

The idea of machine-translation has always been around; the challenge lay in the complexity inherent in any language. Then in the late 1990’s Alta Vista—a search engine that was later bought and merged into Yahoo—launched the world’s first web-based translation tool: Babel Fish. While its translations do present users with a gist of the translated material, it is far from perfect. It doesn’t play well with idioms and is often confounded by homonyms and other figures of speech. Thus language translation remained a dream until Google launched Google Translate. Google Translate, which originally used statistical machine translation, now uses neural machine translation—translating entire sentences at a time, rather than just piece by piece. This allows it to use a broader context to identify the most relevant translation, in effect, allowing it to speak almost like a human and in real-time.

NLP-powered language translation allows you to translate instantly translate content pieces, such as web pages, into selected languages with amazing accuracy.

9. Conversational AI

Conversational AI is the term describing the technology that is at the heart of virtual assistants like Cortana, Alexa, and Siri. These applications rely on NLP to understand the intent behind user queries and generate an accurate response. Such chatbots are immensely useful in answering customer queries, and even assisting in rule-based tasks like booking flights. Their accuracy and 24×7 availability spell lower costs, and higher user engagement. They can be used to excellent effect in reducing workloads on call centers, allowing customer service employees to focus on more complex issues.

Automated Speech Recognition (ASR)

ASR works by converting human speech from analog acoustic sound waves to digital forms that can be read by machines. It splits speech into individual sounds—called tokens—analyzes each sound, and uses NLP + deep learning algorithms to identify the best-fit word in the selected language, before converting the sound into text. Many Smartphones today offer speech recognition applications (Siri, for example) to enhance accessibility beyond texting.

Five Industries that NLP Has Changed Forever

Thu, 27 Oct 2022 07:17:24 +0000

The advantages of having machines perform human actions are quite obvious, and language is one area that has attracted scientists and developers for a long time. The ability to let machines take over certain tasks does more than save us the drudgery of doing it ourselves or employing someone else to do it. It eliminates manual errors, ensures consistency, saves time, and is cost-saving too. Since the early efforts in the 50’s, the art of processing language, or Natural Language Processing (NLP) as we know it, has come a long way and is a subset of Artificial Intelligence that many industries are leveraging in their systems of processes. Almost every sphere of human activity can benefit from NLP, but the following five industries are already at the forefront of NLP usage. Briefly, they are HLS, Retail, Fintech, HR, and Cyber Security.

This article takes a deeper dive into each to understand how each benefits from NLP.

(For a clearer understanding of NLP, read our article: NLP—how it works, and how it benefits you.)

Healthcare & Life Science (HLS)

Medical transcription

Physicians typically dictate processes to a voice recorder. This is a good time saver but the audio file still needs to be converted into text by a human. This makes it prone to human errors, and inconsistencies, and is a cost factor. NLP eliminates all three.

Clinical documentation

On average, healthcare workers spend >4 hours a day entering data into Electronic Health Records (EHR). NLP, in combination with Optical Character Recognition (OCR), can extract specific healthcare data from the EHR or the physician’s notes and feed it directly into a data entry application, for instance, an RPA bot. This is a massive time saving and also improves the data quality by eliminating human errors.

Clinical trials

NLP is also used to interpret text—doctors’ notes, pathology reports, etc.—to identify individuals eligible to participate in a given trial. The NLP algorithm is trained on medical records and research papers to hone the ability to recognize medical terms, understand the general needs of the trial, generate a list of qualifications needed, and then evaluate participants’ applications.

Clinical diagnosis

NLP is being used to create medical models that can recognize disease criteria based on clinical terminology and suggest treatment recommendations.

Virtual therapy

Virtual therapists are a form of conversational AI in healthcare that is being successfully used in mental healthcare. Using NLP algorithms that can be trained on mental health diseases and guidelines to provide cognitive behavioral therapy (CBT) for patients suffering from depression PTSD, and anxiety. Virtual therapists are also being used to engage with autistic patients to improve their communication skills. Woebot is one such chatbot that provides CBT for patients.

Recommender engines work by recognizing similarities and extrapolating choices

Retail

Whether we know it or not, we’ve all experienced NLP at work in retail, in the form of chatbots.

Chatbots have become a basic tool in customer service, thanks to their ability to:

Answer questions

Schedule meetings

Process orders

Track fulfilment

Make recommendations

Onboard new users

Chatbots can reduce dependency on human interaction by a significant margin by automating common queries.

In addition to chatbots, NLP-based virtual assistants too offer many advantages, Such virtual assistants can take the form of a mobile application that customers can use to communicate with the store through text or voice. In-store bots can suggest products, locate a desired product, provide information on promotions, etc.

Market research

Retailers depend heavily on market intelligence to guide their planning and strategy. Conventional methods that rely on human facilitation are vulnerable to bias. NLP eliminates that and is also faster and more accurate. Techniques like web-scraping can help retailers extract data (from blogs, social media interactions, news websites, etc.) as well as product data. This can be combined with NLP to detect market trends and consumer sentiment analysis to help guide decision-making.

Semantic search

We’ve all used the search widget to find what we want while surfing online shops. Many e-tailers are using NLP to power semantic searches to understand the shopper’s requirement and put it into context. This enables retailers to tailor recommendations to match the searcher’s intent, leading to higher sales, and fewer abandoned carts.

Fintech

The business of banking and insurance involves a lot of documentation. From ascertaining creditworthiness to weeding out fraud to generating reports there is a lot of tedious, time-consuming work that goes into the typical workday. NLP offers a way out of the tedium, raising both efficiency and accuracy.

(For an in-depth understanding of the various ways NLP is revolutionizing Fintech, read: How NLP is changing the Fintech world.)

Credit rating

Scoring credit of an individual or an organization is a statistical analysis that lenders must perform to determine creditworthiness before any further loan can be sanctioned.

NLP can play a big role in credit scoring by extracting specific information from unstructured documents like loan documentation, income/expenditure statements, investments, etc., supplemented with information gleaned from personal profiles, such as social media accounts. This information can be fed through an ML algorithm into credit scoring software to determine creditworthiness.

Claims management

NLP—often used in conjunction with OCR—can be used to sift through unstructured and structured text using commonly used terminology to detect needed information to process claims.

Reporting

NLP plus ML can be used to comb through unstructured financial statements and related documents. Such as invoices, payment receipts, etc. The extracted information goes into an RPA bot, or any automated solution, to generate financial reports in pre-customized formats.

Auditing

NLP simplifies and eases the task of financial auditing by automating screening of financial documentation, classifying financial statements, and highlighting similarities and differences in documentation…all of which help detect deviations in financial statements.

Fraud is rampant in Fintech industries, but NLP, in combination with ML and predictive analytics can be used to detect fraud in many ways. For instance, by detecting deceptive communication (use of elevated or reduced frequency of certain words, tone of voice, etc.) and identifying the association between these features and fraudulent financial statements.

With recruitment processes increasingly being conducted digitally, there is a lot of scope for NLP to make a difference. Some fundamental use cases include:

Evaluation of CVs

ML algorithms that have been trained on certain keywords, can be used to extract relevant information and classify candidates based on the similarity between their profiles and the job descriptions of the positions. NLP can also summarize resumes that match desired profiles, allowing recruiters to go through resumes faster.

Virtual hiring assistant

Just like a virtual assistant can be used to perform certain actions using NLP. Virtual hiring assistants, or recruitment chatbots, as they are known, can speed up the recruitment process in the following ways:

Screening resumes

Scheduling interviews

Answer candidates’ questions online

Create candidate profiles—and summaries of resumes

NLP is particularly efficient on interview platforms that allow video recording, uploading of documents such as reference letters and certificates. NLP can detect specific words that reflect desirable or undesirable traits, transcribe video content, and summarize it.

Employee satisfaction

NLP can be used to analyze employees’ happiness on the job, as well as identify friction areas, difficulties, bias, et al. NLP can be used to screen feedback, employee comments on social media, employee websites, etc. This helps HR pre-empt potential conflict, identify training needs, keep employees engaged, and generally optimize work culture.

Cybersecurity

Spam

NLP models trained on text classification can be used to filter out spam emails by detecting words/phrases/sentences associated with spam.

Steps followed by NLP models to detect spam

Data exfiltration

Unauthorized movement of data is an insidious security breach; it includes copying or transferring of restricted data from one device to another. Hackers typically use techniques like domain name system tunneling (DNS queries for information sent from a user’s computer to a DNS server) or phishing emails to gather personal information. NLP can successfully identify such DNS queries, malicious language, and spot text anomalies to detect malware and prevent data exfiltration.

There are many ways NLP can help your business benefit, from enhancing efficiencies to cutting costs. To understand how you can leverage NLP in your organization, contact AAIC today.

How NLP is Changing the Fintech World

Tue, 18 Oct 2022 07:46:28 +0000

The term ‘moonshot’, in technology, refers to an ambitious, groundbreaking project; one undertaken without assurance of short-term profitability, but which, if it works out, can change the world in a definitive way. The term probably owes its origin to the idiom “shooting for the moon”— implying an outside chance of success. AI has always held out the potential for moonshots. And in 2013, the MD Anderson Cancer Center launched its own moon shot: a project that aimed to diagnose certain forms of cancer using IBM’s Watson cognitive system, and recommend treatment. In 2017, after exceeding costs of US$62, the project was put on hold. While this is a sadly familiar ending with most moonshots, the technology itself had a lot of other, less ambitious, uses to offer. In contrast with the long shot of moonshots, there are several lower hanging fruit that AI/Ml technologies offer, and one that has seen the greatest use in recent times is a subsection of AI called: Natural Language Processing (NLP). And the fintech industry has been quick to see its benefits and put it to use.

NLP use-cases In fintech

Credit scoring

One of the most basic uses of NLP is credit scoring. This is a statistical analysis that lenders, banks, and insurers routinely conduct, prior to sanctioning a loan, to ascertain the creditworthiness of an individual or a business. The process was, traditionally, a manual one involving poring through multiple documents, such as income, investments, expenses, etc., and extracting data that can be used to analyze creditworthiness.

NLP streamlines, simplifies, and speeds up the process by extracting relevant data from unstructured documents and feeding it to credit scoring software to determine the credit score. That’s not all. NLP can also be used to extract supplementary information from personal profiles , such as social media accounts, mobile applications, etc.

Managing insurance claims

NLP can be used in combination with Optical Character Recognition (OCR) software to analyze insurance claims. This approach can be used to comb through structured and unstructured text data in order to detect the right information to process insurance claims. The application then feeds the information to an ML algorithm, which labels the data according to the specific sections of the claim application form, with the terminology that is commonly filled into it.

Financial auditing

Financial auditing processes can be simplified and speeded up using NLP to automate certain parts, for instance:

Screening of financial documents

Classifying financial statement content

Identifying similarities and differences in documents

This, also enables flagging of anomalies in financial statements.

Fraud detection

NLP plus ML and predictive analytics can be easily used to detect fraud and misinterpreted information from unstructured financial documents.

In a study, researchers successfully used NLP linguistic models to identify deceptive emails—identified by a certain frequency or reduced frequency of first-person pronouns and specific words, words connoting negative emotions, and action verbs. Using a classifier algorithm, researchers were able to analyze linguistic features of annual reports, including voice, active versus passive tone, and readability, and detect any association between these features and fraudulent financial statements.

Loan & mortgage fraud detection

Loan and mortgage applications see a lot of criminal activity, such as the use of a stolen or fake identity to apply for a loan. Fraudsters establish credit history by using the identity to set up a number of credit lines, and finally max out all the credit lines and disappear. Such applications take a long time to detect and even longer to deny. Using a logistical regression model—easily explainable to regulators—with features like loan transaction details, bank statements, credit score, history on banking activities and credit history, bank account details, borrower’s identity, age, income and asset information, and other public information, the AI-powered model was able to flag fraudulent applications as good or greater accuracy than humans

AI-powered NLP technology can also be used to make data-driven decisions that give businesses a competitive edge. By searching real-time private data stored on platforms like Evernote, OneDrive, and SharePoint, businesses can acquire new customers.

Another business area where NLP solutions can make a huge difference is customer service. One UK high street bank saw this in action when they integrated an AI-powered automatic email responder. The solutions automated common queries, sending them to the right department without manual intervention. This enabled the bank to speed up replies with 95% accuracy. And with more and more training data generated with each interaction the scope for fine-tuning the model is infinite.

Email assistants are also able to conduct sentiment analysis on data, using keywords, phrases, and combinations to identify such aspects as emotion and tone of voice. These findings are helping businesses modify their own interactions, resulting in a much higher level of customer satisfaction

Voice assistants and chatbots

AI/ML-powered chatbots and voice assistants can deliver near-sentient experiences, easing the pressure on human staff, while improving customer experiences.

Conclusion

In addition to the above use cases, there are many, many other areas where AI/ML technologies like NLP can raise competitiveness, improve bottom lines and enhance experiences, not just for business stakeholders and their customers but also for their staff.

NLP in particular can be leveraged on interview platforms to analyze candidate sentiment, screen uploaded documentations, check for references, detect specific keywords that reflect positive or negative behavior, and more. NLP is also particularly useful in such areas as detecting employee sentiment, for e.g. detect job satisfaction, racial/sexual bias, etc. from feedback surveys, social media posts and comments, etc. this can help HR pre-emptively address conflict areas, build trainings, engage employees, and create the optimal work culture.

Are Serverless and Microservices Made for Each Other?

Wed, 12 Oct 2022 07:40:12 +0000

Serverless computing and microservices have a lot in common; both seriously disrupted computing as we know it, both offer excellent scalability, both are cloud based, both are modular in nature, and both help minimize overheads. So are they made for each other? Let’s take a closer look.

Serverless computing describes an approach that abstracts away the server infrastructure relieving developers of the necessity to provision, manage and secure the underlying infrastructure needed to execute code. Serverless computing is best applied in executing smaller segments of code, which are triggered by pre-set events. They are also modular in nature and easy to automatically scale, making them ideal for use in microservices based architecture.

Microservices architecture arose in response to the difficulty—and expense—in managing traditional monolithic applications, which are designed as one integrated and independent unit, i.e. database, client-side interface, and server-side interface all compacted in one unit. Monolithic applications are fairly rigid, making them difficult to scale, update, and manage, except as simple applications.

Unlike monolithic applications that are built as one integrated unit, microservices comprise smaller services—each handling only one service, managed independently, and individually scalable.

For a deeper understanding, read: Monolithic vs microservices: principles and practices

For instance: an eCommerce website built as a microservices application would contain individual microservices for aspects like product catalog, check out, shipping, et al. These microservices can be hosted on containers, VMs, or private clouds. Their modular nature makes them easy to scale horizontally; where, in the event of a peak, monolithic applications would have to be scaled up in entirety, in microservices-based applications, only the service that’s facing the rising demand can be scaled.

Serverless microservices: how it works

Since a microservice may comprise just one or two functions—that are concurrently deployed—each microservice can be broken down to individual functions and hosted on serverless. The serverless function will execute the corresponding code in response to a pre-set trigger: http request, file download or upload, or any other event.

Continuing the above example: when an order is ready to ship, it could trigger a function that checks and confirms the shipping address, this, in turn, could trigger another function that handles printing of a shipping label, and trigger another function, which could send a shipment notification to the intended recipient. This is a fairly simple explanation; in actual implementation one would add things like queues in between for fail safe and to manage the parallel processing limits of the serverless infra as well as the system needs.

Typical Serverless Microservice-based Architecture

Benefits of serverless microservices

Broadly, serverless microservices offer all the advantages that serverless architectures provide, such as:

Lower overheads, possibly lower than container based microservices.
Cost-efficiency—you avoid the cost of provisioning and managing underlying infrastructure, and only pay for what you actually use. i.e.only when the function is called.
Easy scalability—their modularity makes horizontal scaling simple
Manageability—thanks to their modularity
Flexibility: This means that functions used in one microservice can also be extended to other microservice—if it uses the same logic and components. This reduces the amount of code that would otherwise have needed to be written to scale out the application

Use Cases

Any evolving application, especially the complex ones, make good candidates for serverless microservices: their modularity makes them simpler to manage and scale.

If the application can be broken down into multiple, disparate services, and each, in turn, can be split into short, event-driven tasks. Applications that handle consistent loads (i.e. requiring them to be constantly ‘on and being used’, or those that handle long running tasks, would work better as monolithic applications.

Challenges

While serverless microservices allow quick and cost-effective iteration, they do pose a few challenges

Defining function boundaries. It’s important that each function has a limited role, but it’s not easy to define the scope of each function, furthermore stretching your logic across too many functions makes it harder to add updates or add new features.
Cold starts. When and if a function is triggered after a period of inactivity, it can cause higher latency. This can be resolved by keeping those functions ‘warm’. This can raise costs but, depending on the application, can still be more cost effective than a monolithic approach
Complex troubleshooting. One application typically comprises multiple microservices, each of which use functions that are short liven and shares with other resources. This complexity makes it difficult to understand dependencies and trace root cause of errors is they arise.

These challenges can be resolved by a) keeping the occasionally needed functions in a warm mode—for instance, the service handling sign-in or account creation, and b) by migrating to serverless in reverse-priority, i.e. moving non-critical microservices to serverless first.

In a nutshell

Serverless microservices refers to cloud-based services that use serverless functions to perform specific tasks within an application. Serverless functions are modular and, thus, easily scalable, rendering them ideal for microservices-based architecture. This approach combines the benefits of serverless computing and microservices architectures, enabling lower costs, lower downtime, higher scalability, and independent deployment. As with any computing technology, serverless microservices is not a one-size-fits-all solution. Simpler applications, and those with long running services, are more suited for a monolithic design.

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NLP – How it works and how it benefits you

Wed, 12 Oct 2022 07:33:10 +0000

Every time you ask Alexa (or Siri or Cortana, or Google…) to perform a task and it responds in a manner that is almost human, we take it for granted. But there’s a lot that goes on under the hood, and this article takes a closer look at the AI technology that makes human-machine interactions possible: NLP. (Natural Language Processing)

More to AI than Meets the Eye

Human speech is complicated. Not only do we speak in diverse languages like English, Spanish, French, etc. even those who speak a single language natively borrow words and phrases from other languages, almost unconsciously. In addition, there are numerous dialects, and each has its own set of grammar and syntax rules, slang, etc. In addition, we mumble, stammer, and use filler words, like, well, ‘like’. And writing is just as complex with misspelled words, abbreviations, and incorrect or omitted punctuation. Computers on the other hand speak “Machine” a language comprising only zeroes and ones in countless different patterns, bridging the two has only been made possible recently through the availability of big data—and the computing muscle to run complex algorithms to convert communications into zeroes and ones, and produce requested actions.

When a person asks his voice assistant (Siri/Alexa/etc.) to perform an action, they may do so in myriad ways, using different words, phrases, slang, sentence fragments, etc. But the application that runs the voice assistant must understand what is requested by breaking up the language into its basic parts, understand the pieces, and the context that gives it meaning, and respond appropriately…all in the space of a second or two.

How NLP relates to AI

Natural Language Processing is new, but the idea isn’t. The benefits of being able to communicate with machines were always apparent. More than 70 years ago, programmers used punch cards (cards with holes corresponding to zeroes and ones) to interact with computers. It was a tedious and manual process that few understood and fewer could perform. Then in 1952, Bell Labs created the first speech recognition, which could identify all 10 digits. It was called Audrey, but it was slow and thus quickly abandoned.

In 1971, the US DoD agency DARPA (Defense Advanced Research Projects Agency) developed a system, named Harpy, that could recognize more than a thousand words. This was a precursor of real-time speech recognition.

Over the past 50 years, thanks to new technologies, and computing power, coupled with the existence of—and the ability—to handle massive data sets, we have gained the ability to communicate with voice assistants like Siri and Alexa without skipping a beat.

Importance of NLP

Handling large volumes of text

NLP has made it possible for computers to read text, hear speech, break it down into its smallest component parts, understand it, interpret it, and respond. And computers can do it without fatigue or error. This is of huge benefit to many fields of human endeavor, where staggering volumes of unstructured data are generated—from medical records to social media. Automation will enable organizations to go through this data, analyze text, extract the required information, and do so quickly and accurately.

How NLP Works

Step 1. Sentence Segmentation: Breaking down text/speech into its elemental component parts.

Step 2. This is called tokenization: If you’ve ever diagrammed a sentence in school, you’ve done the same job manually. The objective is the same: to understand the component parts, their relationships to each other, and how they work together to create meaning.

Step 3. Stemming & Lemmatization: Stemming simple chops the ends off words to get to the root. Unfortunately, this doesn’t always work, as randomly chopping off the ends sometimes leaves a word fragment that has no meaning. Lemmatization groups words with similar inflextions to get to the root word.

Step 4. Filtering out the noise: We use a number of words that appear frequently and which can be safely filtered out without affecting the statistical analysis.

E.g. words like ‘a’, ‘the’, ‘and’, ‘this, ‘is’…

Step 5. Dependency parsing: This is done to understand how the words in relate to each other

Step 6. Assigning POS (Parts of Speech) Tags: Here each word is linked to its grammatical function to understand context. This is important when you consider how we often use nouns (even proper nouns) as verbs, for instance, “I need to google that.”

Step 7. Named Entity Recognition: In this step, the application detects a named entity, for instance, a person’s name, place, location, organization, etc.

Step 8: Chunking: The opposite of segmentation, chunking involves grouping individual pieces of information to form bigger fragments.

Phases of NLP

These stages can also be interpreted as the following phases

Lexical Analysis: This is the first phase, wherein the source content is scanned and the whole text is divided into paragraphs, sentences, words, and as individual characters

Syntactical Analysis: This is also known as parsing, and is done to understand the relationship between the words and phrases. E.g. Delhi came to John. Clearly, this sentence is incorrect, and is thus rejected by the Syntactic Analyzer

Semantic Analysis: This phase focuses on the literal meaning of the words and phrases

Discourse Integration: In this phase the sentences that precede a sentence and the ones that follow are taken into account to better understand the content

Pragmatic Analysis: This is the final NLP phase. It is done to understand the intended effect through context. For instance: Please open the door, is interpreted as a request, not an order.

NLP Applications

There are many, many general applications for NLP, such as translation, autocomplete, automated speech recognition, and conversational AI/Chatbots. But beyond interacting with Siri or Alexa, NLP offers many practical applications, here are some of the most common.

Spam filtering. If you’ve ever looked through your spam folder and studied the subject lines, you’ll notice a lot of similarities. This is Bayesian spam filtering at work. It is a statistical NLP technique that identifies junk from words in a spam list

Search. Websites that provide a search bar to enable visitors to search for a specific topic are using NLP methods, viz. topic modeling, entity extraction, and/or content categorization.

Transcripts: Popular video channels offer automatic (text) transcripts of the audio, this is NLP at work.

Social media analytics. NLP is routinely used to track awareness and gauge sentiment about topics.

Sentiment analysis

NLP for Fintech

Insurance and financial services organizations generate a vast amount of data—both structured and unstructured—NLP can help them extract relevant information in situations like long term contracts (with many annexes). NLP is also highly beneficial in risk assessment, enabling the organization to extract relevant data, via NLP processes such as entity recognition as well as historical data, credit and account histories, et al, and estimate a candidate’s loan risk. NLP algorithms can save time and improve accuracy in fraud detection, too, by automating some of the processes involved in reviewing loan documentation. Document classification is another area where fintech can benefit from NLP. Algorithms that can identify content associated with a particular type of document and classify it accordingly, will save auditors hours of manual labor. Fintech industries are also prime candidates for NLP use cases like sentiment analysis—to gauge trust sentiments, by extracting relevant information from social media posts, opinions, etc.

NLP in Healthcare

There are several use cases of NLP in healthcare, including clinical documentation, clinical decision support, clinical trial matching, and virtual transcribing, among others

NLP Benefits

NLP tools have never been so accessible: They help business process massive volumes of text data, streamline operations, improve efficiencies, enhance customer satisfaction, and reduce costs.

Text analysis at scale: Data that would take weeks of manual analysis can be processed in minutes or seconds

No bias, no errors: Humans are prone to mistakes and their inherent bias can skew results, NLP tools—once they’ve been trained and running—have neither. And they are indefatigable.

Streamlined processes and cost savings. NLP allows you to scale, 24×7. It also requires you to employ minimal staff. And it frees them from repetitive tasks and the potential for manual error.

Improved customer satisfaction: Connecting NLP tools to your data systems, for example, can analyze your customer feedback in real-time, so if needed you can contact them right away. NLP also helps you understand your customer base, improve market segmentation, and enhance customer lifetime value.

Happier employees: Relieving employees of the tedium of repetitive tasks, allows them to focus on doing their jobs better. It removes fatigue and improves motivation.

Actionable insights: With NLP you can easily break down data related to matters such as online surveys, reviews, etc. This lets you do away with guesswork or cursory analysis, and discover real, actionable insights that can bring real results.

Facilitating DevOps with Application Modernization & Architecture Reboot

Tue, 04 Oct 2022 09:48:16 +0000

The Client

Skyword is a content marketing platform that helps bring brands and their customers closer through high-quality relevant content. Companies can also use the platform to produce and disseminate their own content to improve new customer acquisition, build brand loyalty and sustain interest in what the company does, and how it does it. Skyword maintains a digital publication called Content Standard. The publication offers interesting and original content for public consumption on topics related to content marketing.

Skyword also owns *TrackMaven, *a marketing analytics software that helps marketers compute ROI and improve outcomes on 18 integrated digital and social channels.

Project Goals

Skyword’s IT infrastructure needed to be migrated and modernized. In addition, Skyword was facing challenges in deploying, maintaining, and supporting its existing applications. The company needed to revamp its infrastructure to facilitate automation and DevOps for ongoing upgrades.

The Solution

AAIC engineers team worked with Skyword technical leadership and the Product owner team to develop a modernized business solution which included the following steps:

The application was containerized—i.e. a form of virtualization wherein the application is run in isolated spaces called containers. Containers share OS (unlike Virtual Machines). Everything the application needs to run, i.e. libraries, configuration files, etc., is encapsulated in its container. As a result, the containerized app can run on different types of infrastructure, from bare metal to VMs without any need to refactor it to the particular environment.

Skyword’s application was packaged and bundled as a Docker image before being deployed in Kubernetes (aka, K8s) clusters. This will enable better management, such as rollouts/rollbacks, service discovery, load balancing, storage orchestration, and self-healing (This refers to automatically replacing and restarting containers that fail) and rescheduling containers when nodes die.
Automation and version control are at the heart of all DevOps practices, and to enable this we adopted GitHub as our repository for code and configuration files. These files are also used to implement CI/CD. GitHub Actions would be used to generate Java (JAR) files, which will be packaged as Docker images along with dependant libraries and set up for automated deployment to AWS Auto Scaling Groups on the client’s cloud. (WAR/JAR files are digitally signed to signify the origin of source code, making it easy to developers to identify version, test, and deploy the web application)
To save time that would otherwise be spent in resource management, we templatized setting up of resources using AWS CloudFormation an Infrastructure as Code service that takes care of provisioning and configuring resources, so you don’t have to individually do it each time. CloudFormation eliminates manual errors, speeds up, and simplifies application development, scaling, and application integration.
As part of the testing protocol, we implemented Selenium an open source testing tool to synchronize and orchestrate tests as per pre-set triggers, which were defined in the CI/CD delivery pipeline.
To ensure security was given its just due, we implemented OWASP, an open-source security testing tool. OWASP helps identify diverse vulnerabilities before fresh releases went into production. Monitoring and security go hand in hand, and to enable thorough security, we enabled automated alerts by setting up GitLab with Slack. This would automatically share the status of CI/CD deployments and monitor for failures, reducing the impact on the business. CloudWatch metrics and logs were also used to monitor the system.
From a cost-optimization perspective Lambda functions were written to scale the environment up and down.
TrackMaven dashboard was set up for operations and monitoring

Architecture

Standard 3 Tier architecture was used to save information on clients securely in the Cloud. Clients can log in with appropriate IAM security, Plus Group Rules and Security.

Within AWS architecture, we utilized Amazon’s Elastic Load Balancers combined with Auto Scaling Groups to balance the traffic across multiple instances and automate the creation/termination of instances across multiple Availability Zones.

A number of components were also split out from the current infrastructure to make it more modular and fault-tolerant, while assuring redundancy across several geographic locations.

Tech Stack

AWS Amazon Simple Storage Service (Amazon S3)

S3 is an object storage service. To store the images formatted by the openross application (an fast, open, and dynamic open source image resizer) S3 buckets were used.

AWS Elastic File System (EFS)

EFS is a regional service that stores data across multiple Availability Zones assuring high availability and durability.

AWS Cloudwatch Log Group

Amazon CloudWatch Logs were used to monitor, store, and access application logs.

AWS Application Load Balancer

ALB was used for distributing incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones.

AWS Route53 Hosted Zones

Route 53, highly available and scalable DNS web service, was used for routing internet traffic to Skyword applications.

AWS Secrets Manager

All of Skyword’s application secrets or properties are stored in AWS Secrets Manager.

AWS Key Management Service

AWS Key Management Service, an AWS managed service was used to create, encrypt, and control master keys used to encrypt data.

AWS Elasticache (Redis)

Amazon ElastiCache is a Redis-compatible in-memory data store service. It was used for session management in Skyword’s web application.

AWS Certificate Manager

It is a service that lets you easily provision, manage, and deploy public and private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates for use with AWS services and internal connected resources.

AWS System Manager Session Manager

Session Manager is a fully managed AWS Systems Manager capability that lets users manage their EC2 instances, on-premises instances, and virtual machines (VMs) through an interactive one-click browser-based shell or through the AWS CLI.

AWS Identity and Access Management

It was implemented to manage access to AWS services and resources securely.

Application Modernization: Steps & Benefits

AWS Relational Database Service (RDS) was used for the data layer in conjunction with AWS Aurora, a MySQL-compatible Relational Database Management Service which was designed from the ground up to run on AWS.

Deployment Application Instance creation, launch, and updating is managed using AMI (Amazon Machine Image.) In order to update AMI i.e., modifying WAR/JAR files present in the AMI, AMI bakery is used. It also provides support for deploying it into the respective auto-scaling group. AMI deployment is done in a blue-green fashion where a new instance will be launched and only after launch will the older or previous instance will be terminated. AWS Lambda function has been set up in the environment. The Lambda function is responsible for scaling up or scaling down autoscaling groups. It can be scheduled to scale up or scale down at a specific rate.

Version Control

Git VCS was used to manage the versioning of the infrastructure. a Git repository, with all of the relevant setup & configuration scripts, along with any relevant documentation, provides detailed information on how to recreate the infrastructure from scratch. This, combined with CloudFormation, enables the automated creation of almost all architected AWS services.

Migration Benefits

Significant improvement in Uptime, which can be monitored with 3rd party tools and confirmed. Implementation of the new pipeline helps to reduce time of the deployment from 11 mins to 5 mins.

No single point of failure

Ability to innovate

Being on AWS opens up an array of new services and technologies, from new storage engines like Redshift or services like Amazon Machine Learning or Lambda, earlier implementation was taking hours to send notification during data processing & involved manual efforts to reboot k8s pods. AAIC automated this manual task using a lambda service which reduced the resolution time to 15 mins

Integration to other systems like WordPress and third party APIs are simple with this devops solution.

Cost Optimization

With the deployment and output of AWS Migration Evaluator, Skyword was able to see detailed reports of their inventory.

Utilization of resources was analyzed, and this helped in right-sizing the company’s AWS solutions. Some of Skyword’s existing resources were over-provisioned and right-sizing their resources resulted in significant cost savings.

Approx 30-35% reduction in the cost by selecting appropriate instances and deleting unused resources in AWS. Earlier the pods requested cpu and memory was higher which was resulting in high configuration spot instances, optimized solution given by AAIC resulted in this cost reduction.

About AAIC

The Problem with Microservices

Fri, 30 Sep 2022 09:42:47 +0000

‘Small size’ may be one of its key principles, but microservices are regularly touted as the next big thing, and in a sense, they have disrupted the way we look at application development. To reiterate, the advantages to using the microservices architecture can be listed as follows:

Microservices benefits

Enhanced dynamic horizontal scaling with ease
Improved Management
Autonomy—in choice of technology, language, et al.
Agility
Speedy delivery and deployment—including debugging

Microservices are build for autonomy, specialization, and scalability

For a deeper dive into the microservice architecture, read our article: Understanding Microservices

By design, microservices offer multiple benefits in developing and deploying applications for businesses, and they are surely a big step forward in adopting DevOps as a culture. But that doesn’t make them a silver bullet for all development problems. The benefits above have made microservices the go-to choice for businesses revamping legacy applications. But there are many instances when microservices are not just less-than-ideal, they can be downright counterproductive. Let’s take a closer look…

Challenges to Using Microservices Architectures

1. Deployment Design

Unlike monolithic applications, microservice applications are complex in design, for a number of reasons, such as

Identifying boundaries and connection points between microservices
Determining size
Designing a suitable framework to integrate services
Microservices need to be created within a bounded context, i.e. each microservice must clarify, encapsulate, and accomplish a specific task. This requires each function to be developed with a data-centric view, and this brings its own challenges, because, without the proper logic, the data would be illogical.

2. Security

Because microservices are deployed across servers and cloud environments, there is a greater risk of loss of visibility of application components and loss of control. Not only does this result in additional points of vulnerability, but it is also harder to test integration tests for these vulnerabilities as each microservice communicates with other microservices via different infrastructure layers.

Since data within a microservice framework is distributed, it is tricky to maintain confidentiality of the data. Setting up access controls and administering secure authentication to access individual services is a technical challenge and also increases the surface exposed to attacks.

3. Testing

Since microservices are designed to be independent, each service must be tested individually in isolation, complicating the testing phase of the software development lifecycle (SDLC). The need to consider integrated services and their own interdependencies further complicates this complexity. This increases the integration test time.

4. Operational complexity

Another principle of microservice that brings challenges is the freedom to choose the technology for each microservice. This in itself can make it difficult to maintain operations, in the following ways:

Monitoring challenges: Traditional monitoring isn’t suitable for microservices for one big reason: a request from the user interface has to travel a convoluted path or multiple services before reaching the one needed to fulfill its request. Without the appropriate tools, it is well nigh impossible to identify the cause of any issue.

Complexity in Application Performance Monitoring

Scalability challenges. Microservices frameworks break an application down into smaller interdependent services that are hosted on separate servers. This means several individual components have to be coordinated when scaling in the event of a spike in usage.

5. Communication

Microservices are, in a sense, miniature standalone applications that communicate with each other to fulfill a set objective. This means microservices have to be configured with infrastructure layers that allow resource sharing across services. This is a challenge because improper configuration will lead to increased latency and reduced speed of calls to discrete services. i.e. slow response time.

Complexity and improper configuration increases latency in microservices

When to Avoid Using Microservices

There are three basic situations that one should consider when considering the viability of using microservices or skipping the microservices architecture for a given application.

Undecided domain

Microservices are created within a bounded context. Thus, if it is complex to break down business requirements into specific domains, it will be just as complicated to create optimally sized microservices. This will also bring the challenge of configuring communication between various services. The more complicated it gets, the more difficult to realize real value in the microservices architecture. As the application evolves in the future, it is even more unlikely that your application’s domain will remain unchanged.

Inability to embrace a DevOps culture

One of the big reasons for adopting microservices is the ability to transition to a DevOps culture; the benefits are obvious:

Automation

Reduced cost & effort

Operational efficiency

If transitioning to a microservices architecture will not help achieve these goals, it is better to reconsider adoption. There is no point to add complexity and effort without gaining significant efficiency.

Small application size

If the application is small and the size doesn’t justify splitting into smaller components, skip the microservices architecture. Applications that are already small don’t need to be broken down into smaller components. The very objective of microservices architectures is to break large, complex applications into a loosely coupled application of smaller services. If the application is already small and straightforward, transitioning to a microservices framework will bring unnecessary complexity.

Should You Adopt Microservices?

There is little doubt that the microservice architecture provides a number of benefits, including faster delivery, better software quality, scalability, autonomy, etc. Businesses considering transitioning from a monolithic model to microservices need to conduct careful due diligence to ensure that their objectives will be served. It goes without saying that if the downsides of using microservices outweigh the benefits—factoring in time, effort, and money spent—then microservices are most likely not the ideal solution for the application.

*Still Undecided? *Reach out to our experts to explore whether microservices can be the solution to your needs

A DevOps exercise to cut short deployment time while ramping up security and savings for a leading tech hiring platform

Wed, 21 Sep 2022 11:09:16 +0000

The Client

Cutshort is a tech hiring platform in India. The platform connects employers with top management systems, engages with them, identifies the best candidates, and closes positions in a way that enables hirers to execute their vision quickly.

Cutshort is more than just a networking platform; it uses AI algorithms and gamification to source and connect talent to opportunity. Its high rate of success rate has quickly made it the leading tech hiring platform in the subcontinent.

The Challenge

AI processes were helping hiring automation and recruitment efficiency, but the competition to attract high-quality candidates is fierce, and Cutshort needed its systems to be revamped on a continuous basis to allow fresh updates to the system (for instance, chatbots to collect information beyond resumes, automated scheduling of interviews, etc.). This called for a certain degree of DevOps muscularity.

Migrating the entire solution infrastructure to AWS would take care of availability and scalability in terms of workload, traffic, and peak hours. But it also needed to be cost-effective, and secure. This would be enabled through implementation of AWS security measures on all Cutshort’s AWS accounts with the ability to receive notifications for all alerts.

Cutshort engaged Applied AI Consulting (AAIC) to resolve their challenges and execute the entire project.

Solution

It was clear from the outset that DevOps adoption was the answer to Cutshort’s specific issues. Automation, coupled with quick development and equally speedy deployment would allow them to increase their productivity, optimize processes and enjoy a stronger collaborative work environment.

AWS Partner AAIC kicked off the project by interacting with the client’s engineering, product management, and IT team to fully understand the application’s functional and business requirements from the perspective of the end-user and took the below key steps to provide the following solution:

AAIC redesigned the organization’s IT architecture to include use of ECS Fargate with Autoscaling & SPOT instead of ECS NodeGroups. This will help Cutshort save on costs as well as enable it to scale automatically
Cutshort wasn’t using any Infrastructure-as-a-Code (IaaC) tool; we brought in Terraform, so they could spin up new solution infrastructure in a few minutes and deploy it seamlessly using a CI pipeline.
With the help of Terraform, AAIC created a new infrastructure using Amazon Elastic Container Service for Cutshort which was secured with the help of AWS services and resources such as AWS WAF, NACL’s, and internal load balancers. We also implemented alerts and notiﬁcations using Amazon Cloudwatch and Amazon Simple Notification Service (SNS).
AAIC optimized Cutshort’s AWS accounts by getting them on AWS’s savings plan. This will create significant cost benefits for the organization.
Another step towards helping them in managing their accounts was to set up consolidated billing. Getting one bill for multiple accounts means easier tracking of charges, and scrutiny of cost and usage data. Plus the ability to combine usage across all accounts, allows the organization to benefit from such features as volume pricing discounts, savings plans, and discounts on reserved instances. Since there is no fee for consolidated billing, it was another saving.
Cutshort was using G-suite for various reasons, including email, calendar, docs, etc. We made it easier for them to manage access and user permissions, by integrating it with IAM Identity Center, giving Cutshort’s administrators single sign-on centralized access to all their AWS accounts.

Tech Stack

Outcomes

25% – 30% Cost Savings-

With new infrastructure, and the introduction of features like AWS Savings plan and Consolidated Billing, and their application across their AWS accounts, Cutshort has been able to save 25-30% of their cost compared to earlier budgets.

Speedy Deployment-

Implementation of CodeDeploy and GitHub Actions (A CI/CD tool) has made deployment significantly speedier. Cutshort is now able to move changes to production easily and faster than ever. Earlier production changes happened, at best, once in two weeks but now they are happening in barely two days.

About AAIC

AAIC has a certified cloud engineering team that provides the needed expertise and service to move your on-premises data center to AWS Cloud. It takes the code from existing version control to the open-source library GitHub, debugs, and makes it operational on AWS. During the entire 5 step process, AAIC solution architects and our cloud engineering team work closely with your team to deliver a secure, scalable, and high-performing solution, post-migration.

Monolithic vs Microservices – Principles and Practices

Fri, 16 Sep 2022 11:23:54 +0000

Remember the old black and white flicks where the computer was depicted as a massive machine, usually in the basement, all whirring wheels and blinking lights? Well, those old movies had the size right. Back in those days, we’re talking about the 1980’s here, before computing began to go mainstream, monolithic applications lived on mainframe computers. Then, only organizations with deep pockets could afford these computers. This did give them some advantage but it didn’t come cheap, nor was it particularly speedy, as it often took hours to receive the output of a program. And if the program had a bug, the user would know about it only after a couple of hours. And only after the required corrections would be made, would the computer churn out output.

Those applications integrated everything—including the hardware. UI, business logic, utility programs, and even the database were totally integrated into the application itself. The reason it was so inefficient was that the entire hardware and software were shared by multiple users and background processes.

These applications were the forerunners of present-day monolithic applications, and the inefficiencies drove the creation of microservices architecture. To understand microservices and how they have disrupted IT, we need to understand the differences between each and what led to the move from monolithic to microservices.

Monolithic applications

When the functionalities of a project are integrated into a single codebase, that application can be described as a monolithic application. The clue is in the name itself: *mono, *meaning single (in this case, a single codebase, which contains all the required functionalities for the app to run) Typically, the application is designed with various layers like presentation, service, and persistence and then deployed with the codebase as a single jar/war/lib/binary file. So far so good? Well, not really. There are several disadvantages to this type of application, as listed here:

Disadvantages of Monolithic applications

As organizations grow and their needs evolve, the application becomes too large and complex, making it difficult to manage.

If a single small change needs to be made, the whole application must be retested and redeployed.

With the increase in functionality and size, the application’s start-up and deployment time also increases.

It is very difficult for a new developer (even if his responsibility is related to a single functionality) to understand the logic of a large Monolithic application.

Horizontal scaling is not feasible, as multiple instances of the entire application have to be deployed is a single part of the application encounters large load/traffic. This is very inefficient and resource-intensive.

If, for instance, there is some new technology that is well suited for a particular functionality, it is, nevertheless, difficult to employ, as it affects the entire application, both in terms of time and cost.

Monolithic applications can be unreliable as even a single bug in a single module can bring down the entire application.

But it’s not all doom and gloom. Monolithic applications do have several advantages, which is probably why they are still used.

Easy/straightforward development (compared to microservices)

Easier to deploy, since it uses one single codebase, viz. only a single jar/war/binary file needs to be deployed.

Fewer problems of latency, as all the functionalities exist in one place

Better security (in comparison to microservices), as the application parts are all in a single place.

The disadvantages of monolithic applications led developers to split the services that were integrated into one codebase. This allowed easier scaling and deployment. This type of architecture, which became quite popular in the early 2000’s came to be known as Service Oriented Architecture (SOA).

Service Oriented Architecture

In an SOA, the large application is broken up into multiple smaller services, which are deployed separately. These applications follow a principle known as loose coupling, i.e. the design ensures minimum dependency on each other. This allows services to be used at a specific capacity even if one of the dependent elements goes down.

While the reusable components are a time saver in the development process, it also means that if one component does down, all the systems that use that component will also fail. This is a big point of difference between SOA and microservices. An SOA system handles multiple tasks, whereas each microservice is responsible for only one single task (aka, single responsibility principle). Furthermore, SOA services don’t communicate with each other directly, they used an Enterprise Service Bus (ESB). It is possible to do away with the ESB and have the services communicate directly but this can complicate the development process and raise costs, too.

So, while SOA resolved some challenges posed by the monolithic approach, deployment, updates, scaling, etc. were still an issue. This led to the evolution of the microservice architecture.

SOA vs Microservices: High Level DifferencesSOAMicroservices Each system can handle multiple tasksSingle responsibility principleNot as scalable as microservicesHighly scalable and flexiblePrimarily about resource sharingThe primary purpose is to exclude all dependenciesLoosely coupled (compared to monolithic apps)Significantly more loosely coupled than SOA

The biggest noticeable difference between monolithic applications, SOA, and microservices is the uncoupling of services. Although this is true for SOA as well, microservices are far more loosely coupled than SOA applications, because each microservice is responsible for just one specific task. This is possible, in large part, because each microservice has its own database, unlike monolithic or SOA architectures where databases are shared.

Having discrete databases does lead to some duplication of data, but having a database per microservice ensures loose coupling. Furthermore, since microservices each have their own separate database, they can use the type of database that best suits each one’s needs.

Principles of microservices

Single responsibility: This is one of microservices’ design’s basic principles, wherein each microservice has one and only one responsibility or functionality. In other words, the number of microservices that each application should comprise is equal to the number of functionalities needed.
Decentralized database: As described earlier, this is necessary for each microservice to function independently.
Diverse tech-stack: Given the abundance and diversity of technologies in today’s world, there is always a technology that is best suited to implement a particular functionality, and microservices architectures leverage this to the fullest, allowing developers considerable autonomy if choosing preferred technologies. This was not possible in monolithic applications, and led to compromises, as developers were not able to use different technology for each functionality.

Microservices, on the other hand, are never restricted from adopting the best-fit technology stack or backend database storage to meet the business’s requirements.

Designed for failure: Microservices, ideally, should be designed in a manner where the failure of one does not affect the whole system, or indeed, any other microservice. This was not the case in the Monolithic applications, where the failure of one module inevitably led to the whole application failing.

Microservices advantages

Easy to manage a specific micro-service, given its smaller size
Easy to deploy The entire application doesn’t need to be stopped to deploy an update in one of the microservices; only that particular microservice needs to be redeployed and that too with zero downtime.
Easy to onboard a new developer, as he doesn’t need to understand the whole system, the microservice providing the functionality he is responsible for.
Easy to scale When and if a particular service is facing a heavy load, only that particular microservice needs to be scaled…for instance, shopping carts experience far more loads than say user registration. In this instance, only the microservices responsible for shopping carts need to be scaled. In other words, the microservices architecture supports horizontal scaling.
Freedom to use best-fit tech-stack Since each microservice has a particular function, and the microservice is basically designed for autonomy, there is no restriction on adopting the technology that best addresses the business requirements.
Easy to debug In the event a bug infects the application, just that particular microservice goes down and can be individually addressed. It doesn’t affect other microservices as they continue to provide other functionalities, ensuring the system doesn’t go down as a whole.

Disadvantages of microservices

While there are many clear advantages to using microservices, there are some downsides too.

Increased complexity: As a distributed system, microservices are much more complex than monolithic applications. And this complexity increases as the number of microservices within the application increase.
Specific skill sets: Developers working on microservices architecture must have special skill-sets, such as being able to to identify microservices and manage their inter-communications.
Debugging a microservice is easier: in theory, it can be complicated in practice, as the control flows over many microservices, and pinpointing why and where exactly the error occurred can be a tedious task.
High network demands & increased latency: microservices’ network usage is costly, as the microservices need to interact with each other and all these remotes bring network latency.

Less secure (compared to monolithic applications) because of inter-services communication over the network.

Conclusion

It’s never simple to build any application, and the complexity increases as the application grows larger or more evolved. Monolithic architectures are thus better suited for simple, lightweight applications, while microservices architectures are a better choice for complex, evolving applications. Some experts are of the opinion that it is more logical to start with the monolithic application first; while others recommend going directly with a microservices approach if that is indeed your goal. Whatever the case, it is important to understand the principles governing monolithic architecture as it is the starting point for microservices architecture.

Components of a Microservices Architecture

Tue, 13 Sep 2022 11:45:13 +0000

As one might guess from its name, microservices describe an architecture that is small. It manages this by breaking up large monolithic applications into individual, and independent services that are managed by discrete teams. Each service includes all the elements it needs to run in any environment, including code, databases, application functions, and programming logic, and spreads it across servers and platforms.

To do all this effectively, the microservices architecture requires certain components that help bring all these entities together cohesively across a distributed system. The most important of these components are as follows:

Containers

Although this might sound paradoxical, containers are actually not necessary for deploying microservices; nor are microservices the only reason containers exist. What makes them vital is that they have the ability to significantly improve deployment, save time, and enhance app efficiency, compared to other deployment methods such as Virtual Machines (VM)

The biggest point of differentiation between containers and VMs is that a VM needs a dedicated Operating System (OS) for its use, whereas many containers can share an OS and middleware components. Eliminating VMs eliminates the need to provide individual OS for each small service. This means organizations can run a larger collection of microservices on a server.

The second significant advantage of using containers in microservices is that they offer the ability to on-demand deployment, without compromising application performance.

Containers orchestration can also be automated, this enables configuring, scheduling, management, provisioning, deployment, scaling, and health monitoring of individual containers to be undertaken with minimal effort. There are several container orchestration tools; the most popular of which are Docker (aka, Docker Swarm), an open-source platform for container management, Kubernetes, OpenShift, and Google Container Engine (GKE), among others.

Containers in microservices, thus, enable independence and consistency, which is a critical part of scaling pieces of a microservices architecture — depending on workloads — as opposed to the whole application. In the event of a failure, containers support the ability to redeploy microservices.

Service Discovery (Service Registry)

Unlike in a monolithic application, microservices architectures comprise several smaller applications that work individually to deliver a collectively larger solution. This allows for many big benefits, including rapid and economical scaling—since only individual microservices need to be scaled rather than the entire application itself—quick deployment, monitoring, etc. But to function, the microservices that comprise this architecture must be able to communicate; i.e. call on other microservices as needed. This means they must know where the particular service is deployed. Enter, Service Discovery! Service Discovery is a microservice in itself, and its function is to maintain a register (service registry) of where all the microservices are located. So in one sense, this is actually a service location discovery.

Service discovery patterns

There are two basic discovery patterns: the client-side pattern and the server-side pattern

The client-side discovery pattern: Consider an analogy here. Say you want to order out from a certain restaurant but don’t know their number. So you call up directory services (or Google), get the number, and make the call. In this instance directory services (or Google) acts as the service registry, and you are the client. This is known as client-side discovery. The pattern basically searches the service registry to locate the required service, sends the information back to the client service, and then routes the request from the client to the required service, maybe, using a load balancing algorithm.

The server-side pattern: In a server-side discovery pattern, using the above analogy, you—the client—call up Google, which searches the service registry AND forwards your request directly to the applicable service instance.

It is important that the service registry data should always be current, in order for services to find their related service instances at runtime. If the service registry is down, the entire inter-service communication will crash and so with the app. To avoid this, enterprises typically use a distributed database.

Service Mesh

As microservices grow in features and maturity, they also grow in complexity, with more and more microservices ‘talking’ to each other to deliver a cohesive user experience. But this also means there are many more endpoints and interactions to monitor, secure, and scale. This translates into increased security vulnerability with higher debugging effort and time. Service Mesh addresses this need. Acting at the application level, the service mesh handles service-to-service communication for cloud applications. It overlays existing applications, without them needing to be aware of its existence. It comprises a Data Plane and a Control Plane. The data plane attaches small proxies to each service, these are called sidecars and their job is to handle incoming and outgoing traffic between services.

The control plane manages service-to-service communications, security policies, and monitoring.

The control plane manages and configures proxies to route traffic, and also collects telemetry data to help monitoring

Service meshes eliminate a lot of the manual effort needed for services to interact. As your applications continue to grow, meshes work to maintain infrastructure and ensure that communication doesn’t break down. This makes it easier for DevOps teams to manage Cloud Native Applications (CNA) in hybrid or multi-cloud environments.

Service meshes contain the logic governing communications. Since the logic is in individual services, it increases the portability of individual microservices. They can be moved to a new server, Docker/K9 cluster or a different public cloud platform, without needing to rebuild the application.

By enforcing security policies and managing access (with encryption, where required) and authentication across the environment, service meshes reduce the potential for cyber attacks.

Read also, Monolithic vs microservices architecture (link to article #3)

API Gateway

Microservices are disconnected autonomous services that together make up the entire application (unlike a monolithic app). This means that to deliver the desired user experience the services need to communicate with the outside client, and occasionally with each other. And they do that through an API gateway. API gateways create an abstraction layer between microservices and outside clients.

The API gateway’s main functions center around communication and administrative roles—such as authenticating, caching, and managing requests, as well as monitoring, messaging and even performing load balancing (which typically occurs within a monolithic application). Thus API gateways allow microservices to remain lightweight.

Since services are autonomous they are basically polyglots, in that they are free to use different technology stacks. An API gateway synergizes this by standardizing messaging protocols, thus speeding up communication between microservices and clients and freeing both the client and the service from the task of translating requests written in unfamiliar formats.

In addition, most API gateways also provide built-in security features, which allow them to manage authorization and authentication for microservices, as well as tracking incoming and outgoing requests to identify any possible intrusions.

There are many API gateway options—both open source and proprietary, e.g. from cloud providers such as Azure and AWS—available to choose from.

Applied AI offers an easy-to-use Serverless API generator that comes with clear instructions to set up, configure and secure. The SLS API Generator can be used with a CLI-based approach, or an API-based approach, which enables configuration and development of APIs without the time- and effort-intensive task of writing production code for the API.

Benefits include:

Speedy development/integration

Ease in modifying code, as per entity definition

Use of pre-written test cases to save unit testing time

Code uniformity, standardization (standard libraries)

Lean code that conforms to DRY norms, and coding best practices

For step by step instructions on how to set up, configure and secure your API gateway using the SLP API generator, reach out to our team, today.

Understanding Microservices

Wed, 07 Sep 2022 11:30:30 +0000

A microservice architecture describes an application that is a collection of smaller autonomous, loosely coupled services, which can be managed, maintained, and tested by a small team. This makes it possible for developers to make changes to a service and redeploy it without affecting the entire application. It also allows developers to work in a variety of programming languages—as each service is discrete and interacts with the other services through APIs.

But things weren’t always so easy…

Traditionally, developers built software applications as a unified structure that was self-contained; a single computing network with a single codebase that integrated myriad smaller applications. This was an easy and convenient build, and still is, especially during the initial stages of a project’s life cycle. It allows easier code management, and deployment. It enables a higher speed of development as a result of the simplicity that comes from utilizing one single code base. This led to monolithic architecture becoming the norm. The problems began when applications grew to a size that complicated scaling. Making any change to an application of this size called for the entire stack to be completely updated. To make a single change to a single function called for the stopping of the entire program as developers updated the function and tested it. Developers were forced to build a similar but updated version of the application. This was time-consuming, and resource-intensive and it made deployment a challenge.

Clearly, a disruption was needed.

Disadvantages of a Monolithic Architecture

Slow development speed: The size of a monolithic application hampers development, increases complexity, and slows down the entire development and release process. This makes it complicated, time-intensive, and expensive to implement new features and functions

The key disadvantages can be summarized as follows:

Scalability: Individual components cannot be scaled; the entire structure has to be scaled up, which is an added cost factor.

Reliability: An error in any module impacts the entire application.

Time to release: Any small change would need a complete regression cycle would mean increased time to test and so to release.

Deployment: Any change, no matter how small, requires the redeployment of the entire monolithic application.

Disrupting the Monolith Approach

Then in 2009, a large and growing OTT streaming company did something revolutionary. In a bid to meet the demand for the explosive growth of its video streaming services, the company migrated its entire IT to a public cloud and switched from its monolithic architecture to a structure where services were broken up into smaller individual and autonomous components. In effect, microservices. This was even before the term ‘microservices’ came into being.

That company was Netflix.

Netflix now has thousands of microservices that manage and support separate parts of its platform. And Netflix’s engineers deploy code frequently, possibly a thousand times a day. This just would not have been possible with the earlier monolithic build.

Advantage: Microservices

To repeat, the microservices architecture, (or simply, microservices) relies on a series of independently testable and deployable services. Each of the services points to a specific purpose or goal. They have their own business logic and database. When needed, updates, tests, scaling, and deployment happen within each service.

By decoupling major business applications into separate, independent code bases, Microservices makes it easier to manage.

It must be pointed out that Microservices don’t actually reduce complexity. What they do *do *is make complexity more manageable by splitting tasks into smaller processes that function independently of each other, while continuing to contribute to the overall whole.

This is a huge advantage for DevOps too, as the continuous delivery practice that defines DevOps, aligns with the discrete, de-coupled, autonomous nature of a microservices architecture, allowing teams to adapt quickly to user requirements. In addition, microservices enable a continuous integration and continuous delivery (CI/CD) approach, as developers can now experiment with code and quickly roll changes back if they don’t work as intended.

Microservices resolve a variety of headaches for businesses. Since each of the microservices units run independently, they can be developed, updated, deployed, and scaled without impacting the other services. This also means avoiding duplicate functionality development across the org, other groups and orgs can just use the required microservice. It enables more frequent software updates and improves reliability, uptime, and performance. From deploying updates weekly or even longer, they can now be done multiple times a day. This allows companies to deliver greater value to their customers, faster, and more cost-effectively.

Building robust inter-microservices communicability

For a microservices architecture to function optimally, individual services need to communicate with each other. APIs enable this communication, without it we would just have a collection of disconnected microservices. The alternative is to use a direct client to microservice communication approach. Here, each microservice would have a public endpoint—which would otherwise reside on the API. This would allow a client application to send its requests directly to a particular microservice. This approach might work for a small microservices application, but with large, complex microservices-based architectures, there are some issues that might occur, such as increased latency and complexity, cross-cutting concerns, inability to communicate with services that use non-Internet protocols (like AMQP, or binary).

In such instances, API gateways offer a more functional solution for large or complex applications. It provides a single entry point (aka, backend for frontend). The gateway offers an intermediate layer that makes it convenient for microservice-based application connectivity, avoiding issues such as

coupling (where client apps are coupled to microservices)
multiple round trips (increased latency)
security issues (without a gateway the microservices will be exposed, offering a larger vulnerable surface); cross cutting.

SLS API Generator

Applied AI uses SLS API Generator to enable configuration and development of microservices APIs without the tedium of writing production code for the API, using Dont Repeat Yourself (DRY) patterns. The tool can generate serverless code along with unit test cases as well as CI/CD pipeline —as per your entity definition.. The generated code can be modified by users as per their business logic and then deployed using the generated pipeline.

SLS API Generator Benefits

Speedy development of new services for the entity
Ease in modifying code, as per business logic
Use of pre-written test cases to save unit testing time
Code uniformity, standardization (use of standard libraries)
Lean code that conforms to DRY norms, and coding best practices

*Usage of the SLS API Generator is an important differentiator which helps us to develop production quality serverless APIs for you quickly. Post the Entity/Object Model design it takes us just days to get those APIs deployed with a very good unit-test coverage. *

Also read: Components of a microservices architecture

Microservices Advantages

Agility: Small, independent teams can build and deploy quickly and frequently, which promotes agility.

Flexible scaling: Like a monolithic application, microservices too can reach load capacity. The difference is that when and if this happens new instances of that service can be deployed quickly, relieving pressure. As a multi-tenant and stateless entity with customers spread across multiple instances, it can support significantly larger instances.

Continuous deployment: Release cycles are now faster and more frequent. Updates can happen multiple times a day, instead of weekly or longer. Very good test coverage improves this speed further.

Highly maintainable and testable: Being able to experiment with new features, and roll back if they don’t perform as intended, makes it easier to update code, isolate bugs and fix them. And it reduces time-to-market for new features.

Independently deployable: As microservices are individual (independent) units they can be quickly deployed.

Technology flexibility: The autonomy and independent nature of microservice architectures gives teams the freedom to select the tools they desire.

High reliability. Changes can be made to a specific service without bringing down the entire application.

All these advantages do not make microservices the ideal one-size-fits-all solution. Not all organizations can—or should—implement microservices. Moving from the monolithic architecture’s one, or a small number of codebases microservices, which require many more distributed systems and services, can give rise to unintended complexity, and can reduce the speed and flexibility that microservices were designed to deliver. Plus, it can be a challenge to determine how the application’s sub-services should be split, how different components relate to each other, who owns a particular software component, etc.

Thus, smaller companies—a single product company, for instance—would not need a microservices architecture at all.

Typical Disadvantages

Development sprawl: Compared to monolithic services, microservices add complexity, as there are more services in more places created by multiple teams. This results in slower development speed and sub-optimal operational performance.

Higher infrastructure costs: Every new microservice comes with its own set of requirements, such as test suite/s, deployment playbooks, hosting infrastructure, monitoring tools, etc., which can bring up costs significantly. (Going serverless would help immensely in this though).

Organizational challenges: Development teams need to communicate, and collaborate to effectively coordinate updates, which adds another level of communication.

Debugging challenges: Every microservice has its own logs. This makes debugging more complicated. Plus, if a single business process runs across multiple machines, debugging will be further complicated.

Lack of standardization: Autonomy is good, but it can also lead to a proliferation of languages, logging standards, and monitoring.

Lack of clear ownership: The addition of more services will also increase the number of teams running those services. At times it can become difficult to identify the available services a team can leverage or whom to contact for support.

Recognizing the Need to Migrate to a Microservices Architecture

Most projects initially start out as monolithic and over time, as new features are added, evolve into a microservice architecture.

When this happens it may start to become cumbersome to have many developers working on a single codebase.

Code conflicts will also arise more frequently; plus there is a greater risk of updates to one feature introducing bugs in an unrelated feature.

When and if you notice these undesirable patterns, it might be time for your company to consider migrating to microservices.

Unsure if and how your organization can benefit from microservices? Reach out to our team for a no-commitment presentation, at your convenience.

Connect with Top AWS Professionals

7 Crucial DevOps Practices You Should Know

Tue, 30 Aug 2022 11:38:05 +0000

DevOps is not a buzzword! It’s a transformational perspective.

As per the study, 86% of organizations say it’s important for their company to develop and begin production of new software quickly.

While only 10% say their company is very successful at rapid software development and deployment.

This gap gives a competitive advantage to whoever adopts the right approaches.

And through this blog, we are going to help you make the right decision through 7 DevOps practices

Before that, let’s understand how exactly DevOps work.

How Does DevOps Work?

In the core, when you incorporate DevOps into your organization the development and operations teams are no longer “siloed.”

And how it works can be better understood by the DevOps lifecycle.

A DevOps lifecycle essentially breaks the SDLC into the following stages:

Seeing the above lifecycle, you might get the impression that DevOps isn’t an extra step; it simply makes the process continuous.

7 Crucial DevOps Best Practices You Must Know

If you want to build a successful DevOps strategy then you must invest in cultural changes, automated processes, and the right technologies.

Here are the DevOps best practices that we should be aware of:

Continuous Integration

This practice encourages developers to regularly merge their code changes into a central repository. Then after the automated builds and tests are run. Its goal is to find and fix bugs faster, improve software quality, and reduce the time it takes for new updates to be validated and released.

Continuous Delivery

In this practice, code changes are built, tested, and prepared for production release automatically. As a result, all code changes are deployed to both a testing environment and a production environment once the build stage has been completed.

Microservices

It is an architectural approach to building applications where pieces of an app work independently, but together. This practice involves building a single application as a set of small services. Each service runs as a separate process and communicates with other services through a lightweight interface, typically an HTTP API.

Infrastructure as Code

It’s a practice of provisioning and managing infrastructure using code and software development techniques such as version control and continuous integration. Instead of manually setting up and configuring resources, the cloud’s API-driven model allows developers and system administrators to interact with infrastructure programmatically and at scale.

Configuration Management

It is the practice of maintaining the state of the system’s resources such as virtual machines (VM), servers, and databases. Teams can automate and standardize configuration processes by using configuration management tools. It is often used in conjunction with infrastructure as a code to manage configurations.

Version Control

It is the practice of managing software code in versions. This helps the development team track changes and revisions. That makes it easy for developers to review and recover the code.

Version control is typically implemented using Git, which allows multiple developers to collaborate on code. Each developer works on their copy of the code, and any changes made by any developer are not merged within the master branch until the repository owner approves them.

Continuous Monitoring

OnOnef the critical DevOps practices that provide complete visibility into the health and performance of the overall application.

It improves the visibility of a system’s operations, particularly those that can lead to security breaches. With this level of visibility, the DevOps team can set up alerts and work to resolve issues as they arise. Teams can also identify and resolve performance bottlenecks before they cause further potential damage.

While DevOps adoption is essential for organizations today to stay in the competition. But to beat the competition you will need the right DevOps partner.

After helping companies from various industries we are ready to make your DevOps adoption journey successful.

Are you ready to accelerate your time to market through the strategic implementation of automated tools and pipelines?

Reach out at info@appliedaiconsulting.com

Containerization Experts Increased Developer Velocity by Reducing 50% Scrum Time

Wed, 17 Aug 2022 12:17:07 +0000

Developers who were spending more time on container-related issues are now able to focus on Application Features

Table of Content

Introduction
How we Accelerated Dev Productivity by Providing Ops Support for Kubernetes
Questions You Should Ask Before Initiating Your Containerization Journey
Key Benefits of Containers & Containerization for Businesses
Microservices – Standardization and Technology Infrastructure
Security-oriented DevOps mindset
Specific Containerization Challenges Our Client was Facing And How we Resolved Them
Three main reasons for Container-related Issues
Secrets of our Containerization Expertise
Gene Kim – Amplify DevOps Success with Containers

Introduction

In this article, you’ll learn how we helped an identity management firm save more than half of their scrum time and focus more on their development features.

Plus, our Secrets as a Containerization Experts!

You will also get to know some basics, while we share techniques to embark on your containerization journey with a leading orchestrator –“Kubernetes”.

Before that, let’s face this!

Over the last few years, the container space has exploded in popularity.

Kubernetes and Docker have ascended to the ranks of the most popular technologies in the tech world.

And while developers have adopted containers to increase reliability and scalability. There are many unspoken reality checks.

Let’s see a real-world example,

And understand that even if you started out with a “container-first” approach, what might hold you back.

And how taking assistance from containerization experts can boost your developer’s velocity.

It’s time to get some background.

Increase your Developer’s productivity by 96.5% with DevOps-in-a-box

How we Accelerated Dev Productivity by Providing Ops Support for Kubernetes

A leading Identity management firm from Bengaluru has started its containerization journey with the intention to achieve isolation, security, agility, and portability in its development.

But soon things started to change.

Their teams started spending more time on container-related issues than focusing on the features itself.

You might be wondering how come a secure and agile solution started becoming a bottleneck for them!

Well, let’s look at the below 2 questions to understand the answer:

1.) Before adopting the “container-first” approach, Have you done some research on the key aspects of the application containerization journey?

Are you equipped with the right expertise to manage containers, not just create them?

Here, to give you a brief on the containerization journey that comprises three major stages:

And below is the question list you should consider.

Questions You Should Ask Before Initiating Your Containerization Journey

Are there any applications in the business portfolio that can be containerized?
Which container platform serves best for your enterprise?
Is it worth containerizing all the applications?
What extensions will you need in the container platform to accommodate the applications?
What will the upskill path be for developers?
Will the containerized applications be able to meet existing Service Level Agreements (SLAs)?

And as you can see, we have our own set of questions during the evaluation process. I.e:

Is the app pre-packaged as a single binary or jar file?
Is the platform on which your app is built available in a containerized version or package yet?
Are any of your 3rd party apps available in a container version yet?
Is the app stateless?
Is your application already part of the continuous integration, continuous deployment pipeline yet?

Answers to these questions help us better strategize steps for you.

Would you like to explore our DevOps services along with the toolkit expertise? Click here

To those who don’t have a clear idea of why businesses choose containerization, here is a quick brief:

Containers allow numerous apps to run on a single virtual machine (VM), reducing the number of software licenses required for an enterprise organization to develop in a container environment.

And how do they accomplish this?

They do this by sharing OS kernels, instead of requiring their own.

That further makes containerization a more resourceful approach, both financially and computationally, to enterprise software development.

So, keeping that in mind, businesses see the following key benefits that come with Containerization.

Key Benefits of Containers & Containerization for Businesses

Faster startup times
Less redundancy
Improved security
Better resource distribution
less space requirement, usually measured in MB rather than GB
Consumption of minimal amount of resources.

However, the bigger purpose of businesses today is to move to microservices based architecture to achieve the desired scalability in their applications.

Let’s take a closer look at the microservices and how Containers and Kubernetes are at the core of them.

Microservices – Standardization and Technology Infrastructure

Microservices have become a popular architectural pattern in the last decade or so.

A key part of this evolution has been the use of containers and container orchestration tools like Docker and Kubernetes.

Need not to mention that with microservices, we divide a large application into smaller units that can be released separately. That gives businesses an edge to innovate continuously.

And when we look at the core of the microservices architecture then 2 very crucial aspects come to light:

Standardization
Technology Infrastructure

That’s where docke r and kubernetes come in.

Docker enables standardization!

It does not matter which language you use to develop your microservice whether it’s java or python you can create a docker image and you can treat that the same way.

This standardization also applies when it comes to observability.

And Kubernetes enables the technology infrastructure that is needed to run your microservices.

So, whether you need a naming server or a service registry, Kubernetes has such features built in.

Do you seek AWS partners? Our AWS experts have assisted numerous businesses in scaling their operations. Explore here

Security-oriented DevOps mindset

Let’s say, you are an enterprise and want to switch from VMs to using containers to host your application code.

Because containers can be deployed almost anywhere, they allow you to streamline the application development workflow.

However, The flexibility of being able to deploy virtually anywhere brings attack services around your container environment.

These include the images you use to build your containers, the image registries we use to store our images, and our container run times.

Also, the orchestration platforms like Kubernetes which we use to manage the lifecycle of multiple containers.

However, by prioritizing releases and new features, security measures often lack.

But this is where we at Applied AI Consulting practices the DevOps mindset with the focus on securities.

Our DevOps experts integrate security objectives into continuous delivery processes that reduce the setbacks you might face in future.

As a result, the amount of time spent on resolving security issues has been halved.

Additionally, we have reduced cost by 40% and achieved 90% automation with AI-powered testing and pre-designed pipeline.

Now, going back to our original story. The Identity management firm reached out to us through a reference from an existing client.

And because they have already been introduced to our DevOps expertise as well as our certified Kubernetes team.

They were confident that we could assist them in making things right with our containerization expertise.

We worked with them, understood their containerization-related issues, and resolved them.

Specific Containerization Challenges Our Client was Facing And How we Resolved Them

With Our Kubernetes Expert Solution

Now, the question arises why did our client get stuck in the above challenges?

Well, there are 3 key reasons for that.

The application container was not set up in the right way.
The utilities were not available – to read the logs, start the container, report the container, etc.
The focus on feature delivery is so high that there was no pressure on solving container problems which is usually not the true skill of many teams.

You have come a long way, and

It’s high time to reveal the Secrets of our Containerization Expertise

We have certified Kubernetes administrator and application developer with amazing detail-oriented skills.
We take prime attention while setting permissions, limiting resources, and monitoring systems that help us build a differentiated strategy.
We optimize the builds for the build cache that speeds up the workflow

Check out our Success Stories to learn more about the numerous ways we assist businesses.

Gene Kim – Amplify DevOps Success with Containers

As per Gene, Containers are kind of a technical miracle. He shares how containers can decrease lead times and increase feedback to developers.

A senior director of engineering was facing a biggest problem in her recent project, let’s call it X.

So whenever a developer made a code change to X it would require spinning up 45 VMs, and it would take 30 minutes to get the X instance running.

Then they moved on to a project that essentially put the entire X instance into containers so that they could run them on every dev laptop.

After that, whenever they made a change it would take about five minutes to get the new instance running.

Quality went up, lead time went down, and the developers were finding issues faster and fixing them faster.

You couldn’t do that before containers.

This is all from our end today to help you educate and innovate through containers.

And if you are embarking on the containerization journey we can help you do it right for the first time.

Furthermore, If you are facing similar issues we shared above, reach out to us at vijayroy@appliedaiconsulting.com

S.NoChallengeSolution1The client is having separate K8s clusters for the different development teams and each team develops the microservice as per their requirement but database structure is common for all the teams. If there are database changes done by the developers on the individual clusters but those changes are creating the application breakdown then they have to reset the database.

We reset the database to reference DB using an automated pipeline.2Again, because database structure is common for all the teams. If one team has made some changes in the database and those changes are required by other teams then they have to migrate the entire DB from one cluster to another.

Build a pipeline to Migrate the database from one cluster to another.3Different teams require fresh clusters for testing the application so they have to create the ad-hoc clusters.

Created an automated pipeline for EKS cluster creation and deployment of required microservices on those clusters.

4Initially, secrets were deployed using shell script but it was very difficult to maintain so there was a need for an automated pipeline.

Deployed the secrets using an automated pipeline.5The development team requires a centralized log for identifying the bugs and errors.

Used fluentd to send the application logs to a centralized location and display the logs on the application page so that the development team can see the issues/errors.6Analysis of memory utilization for performance testing.

Created heapdump for the running pod using an automated pipeline.7Checking different metrics from performance testing is important so we have to set up this.

Integrated prometheus and grafana for monitoring the different metrics for performance testing.8Pods were getting restarted due to improper memory assignment.

Fixed the restart issues of the pods by optimizing the memory requirement.9

As the client is having many clusters so monitoring the individual cluster is a very challenging task.Installed botkube for monitoring of the cluster and timely notification on the slack.10Application scalability and cost optimization were important for the application.

Implemented Kubernetes horizontal pod autoscaling (hpa) and autoscaler for the cluster so that all the requests are processed without any issue.

11The client was getting a docker pull rate limit error as they were using community edition.

Enabled Gitlab dependecy proxy pull through the cache to avoid the docker pull rate limit issue.

Now, the question arises why did our client get stuck in the above challenges?

Well, there are 3 key reasons for that.

The application container was not set up in the right way.
The utilities were not available – to read the logs, start the container, report the container, etc.
The focus on feature delivery is so high that there was no pressure on solving container problems which is usually not the true skill of many teams.

You have come a long way, and

It’s high time to reveal the Secrets of our Containerization Expertise

We have certified Kubernetes administrator and application developer with amazing detail-oriented skills.
We take prime attention while setting permissions, limiting resources, and monitoring systems that help us build a differentiated strategy.
We optimize the builds for the build cache that speeds up the workflow

Check out our Success Stories to learn more about the numerous ways we assist businesses.

Gene Kim – Amplify DevOps Success with Containers

As per Gene, Containers are kind of a technical miracle. He shares how containers can decrease lead times and increase feedback to developers.

A senior director of engineering was facing a biggest problem in her recent project, let’s call it X.

So whenever a developer made a code change to X it would require spinning up 45 VMs, and it would take 30 minutes to get the X instance running.

Then they moved on to a project that essentially put the entire X instance into containers so that they could run them on every dev laptop.

After that, whenever they made a change it would take about five minutes to get the new instance running.

Quality went up, lead time went down, and the developers were finding issues faster and fixing them faster.

You couldn’t do that before containers.

This is all from our end today to help you educate and innovate through containers.

And if you are embarking on the containerization journey we can help you do it right for the first time.

Furthermore, If you are facing similar issues we shared above, reach out to us at vijayroy@appliedaiconsulting.com

How to Create an API-Based Integration Gateway Utilizing the AWS Platform

Wed, 17 Aug 2022 11:53:48 +0000

If your platform provides an integrated payment solution. And for each individual client, if you are building and maintaining point-to-point integration then this blog is a must-read for you.

We had a client who was spending ~8 weeks to ~12 weeks for 3rd party ERPs and payment integration with the Process & workflow automation platform.

That was ultimately limiting their potential to serve the number of clients at a time.

So we decided to help them by building an integration hub like Zapier.

Now the question arises why do you need an API gateway in the first place?

Why do we need an API Gateway?

It is crucial to have an API gateway since it provides a unified point of entry for internal APIs. Using it, you can control user access. Moreover, it applies security policies, such as OAuth or JWT, and enables security measures, such as rate limiting. The API gateway is particularly important for securing microservices.

Leveraging AWS Platform and AWS Partner to Build an Integration Hub

AWS provides all the go-to solutions for building a cost-effective and reliable solution on the cloud. And building an integration hub is no different.

And being an AWS advanced consulting partner, AAIC showcases expertise with:

Amazon API Gateway Delivery
Amazon Cloudfront Delivery
AWS Lambda Delivery
AWS WAF Delivery
AWS Cloudformation Delivery

How Applied AI Consulting builds API-based Integration Gateway Powered By AWS Services

AAIC built the Integration Gateway with 4 main components: Flow, Filter, Transformer, and Adopter.

Using the component, inputs are taken from the client’s platform and transformed via an adapter that further talks to multiple ERP systems such as Microsoft Dynamics GP, Oracle Netsuite, etc.

Putting it more specifically,

Flow is created using one or more filters, transformers, and adopters. And each flow has a unique id.
The inputs are filtered through the Filter state, and based on the status condition (Like if something needs to be done or not) they are sent out to the transformer.
Then the transformer will look at the config mapping and transform the input JSON to the expected for as required by the adapter and corresponding action.
Finally, the adopter component will have the actions like creating invoices, creating vendors, creating employees, and more. Authentication can also be an action since it would be specific to the ERP in context.

Benefits of the API integration Gateway

It takes less than a week to go live for any of your customers.
It brings the ability to onboard 80% more customers with varied ERPs.
It’s a no-code low code reusable integration framework.
It abstracts the complexity and helps them integrate with existing ERP via a few clicks on UI.
Boosts the efficiency of businesses with ease and flexibility.
Automation of more than 30 tasks.

Well, we offer enterprises the best-in-class cloud deployment, CloudOps, Cloud Integration, Networking, and data engineering services over AWS. Hence, if you are facing any challenge in your scalability do reach out to us at vijayroy@appliedaiconsulting.com

7Targets Goes Serverless with Applied AI’s AWS Expertise

Fri, 08 Jul 2022 12:07:08 +0000

Executive Summary

AAIC, an AWS Advanced Consulting Partner, is using its AWS Service Delivery Program authorization to accelerate business growth and build its client confidence. The company’s strength is in its AWS skills and expertise through which it helped a leading sales tech platform in going serverless with zero deployment failure within 3 months.

About Client – 7Targets AI Sales Assistant

7Targets is a leading sales tech platform focused on sales force efficiency and productivity using its patented AI-based lead nurturing solution. It has a flagship product ‘AI Sales Assistant’ which can write emails and do follow-ups with the leads as well as read the received responses and act accordingly.

Expertise with Different Service Delivery Programs makes Applied AI Consulting an Effective AWS Partner

AAIC is an AWS advanced consulting partner with expertise in AWS lambda as well as other services. Being a Cloud System Integrator (CSI), it enables enterprises to create their application and big-data workloads safely to the AWS cloud with improved governance.

Applied AI Consulting has deep knowledge of Native AWS services and the best practices. AAIC has worked with scores of customers for service delivery.

Its 20+ AWS certifications and 7+ active cloud projects, make it a reliable partner for cloud-based projects that effectively showcase its expertise with:

Amazon API Gateway Delivery
Amazon CloudFront Delivery
AWS Lambda Delivery
AWS WAF Delivery
AWS CloudFormation Delivery

“We offer enterprises the best-in-class cloud deployment, CloudOps, cloud integration, networking, and data engineering services over AWS. And our growing customer base and positive feedback from them is a clear indicator of service excellence .”

Vijay Roy, CEO, Applied AI Consulting

AAIC Identifying 7Targets Challenges

7Targets provides an email-based AI Sales Assistant and a SaaS solution. The infrequent load of processing the received email via an event-driven architecture was one critical need. Apart from that, there was the requirement of externalizing the processing of sending and receiving emails. And that with a possibility of managing (start, stop, restart, etc) each of the leads and emails for supportability reasons.

Here are a few of the challenges from them

The cost of running was not in control and they needed pay-as-you-use mechanism without losing the security and elasticity aspect.
Their process lacked of an externalized way to manage the flow for sending and receiving emails.
Their existing monolith service was behind the load balancer with autoscaling, but the scale-up and scale down was not helping as the load was very infrequent throughout the day, multiple times.
They needed upgrades with zero downtime.

Ultimately, they were looking for an AWS Partner who is adept in native AWS services. Specifically, Amazon API Gateway, AWS Step Functions, AWS S3, AWS Cognito, AWS Lambda, Amazon CloudFront, AWS WAF, Amazon Comprehend, Amazon ECS Fargate, and Amazon Cloudwatch. And AAIC fulfilled all their requirements criteria.

AAIC Resolving 7Targets Challenges and Helping it Go Serverless with Zero Downtime

After understanding their challenges, the code structure, and the different microservices, the Applied AI team proposed the utilization of different AWS services to best suit their requirement.

Some of the major steps taken are:

Creating the step functions( state machine process lead, process lead response, etc) with various AWS lambda functions for each step. Where we used Python runtime and serverless.io framework. This gave the required control the customer needed for externalization of flow.
Sending the accepted received email in the S3 bucket using SES and processing them using lambda and step functions.
Processing the received response and categorizing based on an ML model running in lambda and certain properties available in the response like date, etc helped them keep the cost of the ML stack low.
To provide ease of development and deployment without any downtime we used Lambda functions and API Gateway.
Used Amazon CloudFront to Improve the access speed for downloading the content for their customers.
Helped them go serverless with Lambda, Step Functions, DynamoDB, AWS ECS Fargate, and RDS.
We used Python as the primary language which gave them confidence as the skill was not new to them.
Deploying and using the ML model from Lambda helped them get valuable inferences at a minimal cost.
Also, our usage of Comprehend gave them some quick wins.
Automated test to verify before deployments.
Additionally, we used AWS data migration services, ECR, and AWS cloud formation for utilizing related AWS and third-party resources.

Why AWS?

It’s a no-brainer that AWS is packed with all the go-to solutions for making a cost-effective and reliable solution on the cloud. Companies of all sizes understand it and use it to scale and resolve their various challenges. Some of the core reasons for 7Targets to choose AWS are mentioned below:

AWS services seemed more mature to the client to improve their current situation. Like AWS WAF, load balancers, step functions, secret managers, and more.
The client already knew about the AAIC and its advanced consultancy partnership with AWS.
The team (both 7Targets and AAIC) had experience and expertise on AWS.

Why the Customer Chose AAIC as their AWS Partner

The following factors influenced their decision to choose AAIC:

AAIC’s serverless-api-generator tool that generates production-ready REST API code along with UTs and the pipelines. The AAIC team had many services on serverless beyond the ECS. And the client knew this generator would be useful for getting pipelines quickly.
Usage of aws_lambda_powertools ensured the customer that AAIC brings the required standards too.
The AAIC AWS-certified and experienced team of cloud engineers.
AAIC’s similar previous work helped them take this decision.
Also, AAIC’s DevOps accelerator [DevOps-in-a-box] that potentially reduces the DevOps pipeline implementation by 60% got their attention.

Future Plans

The AWS partner and the client continue to work together. Keeping a tab on production and MLOps is key for the client, which AAIC helps with. Cost optimization is a continuous process and AAIC does great there. Also, new feature development and maintenance of existing is a critical responsibility.

As the 7Targets production DB and usage increases, they need to continue to improve on using properly sized resources, upgrade databases, etc, and AAIC helps there.

AWS Services Used as Part of the Solution

AWS S3, AWS ECR, AWS Lambda, AWS ECS Fargate, Amazon CloudWatch, AWS Secrets Manager, Amazon SNS, Dynamo DB, AWS step functions, AWS WAF, Cognito, RDS.

Benefits

Usage of serverless for inference using the ML model has helped the customer from a cost perspective.
Usage of Lambda and Step function that scaled easily to their increasing load with increasing customers.
The monitoring dashboard using AWS Cloudwatch gives a very clear picture of the workload and health

7Targets Solution Architecture

Want to Implement a Secure and Scalable Infrastructure on AWS?

Mon, 20 Jun 2022 06:19:41 +0000

As per statistics, Scalability and Security are two major concerns of any growing company. In this blog, we will focus on the Scalability challenges and a proven strategy to deploy your solution infrastructure on AWS with all the security measures supported by AWS.

Scalability issues arise when an application receives more users than expected. At that point, the tech team begins to look for someone who can magically add more nodes to the system and scale the application.

However, this is not the right way to approach scalability. Decisions for scalability should be made early in the development process. Even before you’ve decided on frontend and backend technology.

Well, As a result of scalability issues, a tech hiring platform came to us with the idea of deploying their infrastructure on AWS. They wanted their solution to be highly available and scalable based on the workload, traffic, and peak hours.

Tired of manual Testing? Try AITest!

Benefits of Deploying Your Solution Infrastructure on AWS

Over 7,500 government agencies and 5,000 educational institutions use AWS, isn’t it worth considering? Check out the incredible benefits AWS brings to your organization:

How to Implement a Secure and Scalable Infrastructure on AWS

Consider the following steps to implement a secure and scalable infrastructure on AWS:

Create an architecture using tools like ECS Fargate with Autoscaling & SPOT instead of ECS NodeGroups to save the cost as well as to scale automatically.
Introduce Infrastructure-as-Code (IaC) tool like terraform to spin up a new solution infrastructure in a few minutes.
Consider the AWS savings plan and how it can benefit the overall cost.
If required, set up the AWS organization for consolidated billing.
Integrate the G-Suite with AWS Single-Sign-On to manage access centrally across their AWS Organization
Create a new infrastructure that was secured with the help of AWS services and resources such as AWS WAF, NACLs, and internal load balancers.

Bonus Tip: Implement alerts and notifications with the help of AWS Cloudwatch and SNS.

Would you like to get into the architecture diagram? See here

Scalability is a mysterious and powerful magic everybody wants for their business. And AAIC has the AWS magicians in its team who help you scale on-demand Using AWS tools, Auto Scaling, and Elastic Load Balancing.

Besides AWS expertise, we help companies master performance and collaboration through our DevOps services.

Want to know more? Book a free 30 minutes consultation call

Steps for Successful Migration from GitLab to GitHub

Mon, 20 Jun 2022 06:08:35 +0000

Are you concerned about not being able to host everything on GitLab?

Well, you are not alone.

Many businesses face this challenge.

You may have used a self-hosted GitLab instance to host your software repositories because of the obvious benefits it promised like:

No limits on the number of (private) repositories or amount of storage.
Full control over the hardware.

However, when the hardware underlying the self-hosted instance reached end-of-life, and limited time for maintenance starts creating more mess than good you might look for another solution. Right?

This is what happened with a leading content marketing platform and we helped them Migrate from GitLab to GitHub successfully.

Would you like to read the whole case study? Get it Now!

Why Migrate From GitLab to GitHub?

Both GitLab and GitHub are designed to make it easy for developers, designers, and team members to collaborate on the same project. It ensures everyone has the same access to the latest code and all modifications are tracked.

Open-source repositories are supported by both, but they use different programming models. And to help you understand why companies choose to migrate from GitLab to GitHub, Here’s a breakdown of a few challenges with GitLab and the benefits of using GitHub.

GitLab Challenges

GitHub Advantages

Inability to host everything on GitLab.
If your client’s team is familiar with GitHub, then onboarding them on GitLab will take much effort.
GitLab’s slack integration isn’t efficient enough.
Unlimited public/private repositories.
Unlimited collaborators.
Easily works with a third-party CI program like Jenkins, CircleCI, or TravisCI.
Emphasizes speed.

Steps to Successfully Migrate from GitLab to GitHub

Based on the below migration process, We at AAIC, follow the entire migration planning and execution steps to successfully migrate Gitlab Code Repositories to Github.

There are certain aspects to this process that are crucial. Mentioning below the points we put special attention to

Generation of Artifacts like jar or war files.
Understanding and deploying to appropriate environments.

You might have got the idea of how the GitLab to GitHub migration process works. Our team at AAIC makes it easier for you through the well-architected frameworks. Besides migration, we help companies master performance and collaboration through our DevOps services.

Want to know more? Book a free 30 minutes consultation call

Are You Affected by Employee Swipe-in/swipe-out Latency Issues?

Mon, 20 Jun 2022 05:59:07 +0000

If you are in a position of leadership in a business where employees’ time on the floor (ToF) and time on campus (Tic) values are important in measuring business revenue. And you’re experiencing latency issues as you process data from various controllers.

Then this particular blog is for You!

We had a client who was operating in an IT service industry with over 2 lakh employees and clients on six continents.

They were facing a one-day latency in processing 1.8 lakh+ employees’ swipe-in and swipe-out data from various controllers. That’s why they approached the AAIC team and we have helped them have a robust solution.

Interested to know more? Stay tuned!

Want to Scale Your business? Hire experts

The Challenges Faced Because of the Swipe-in/Swipe-out Latency Issues

You may be familiar with the tedious process of extracting data from various controllers. Not just that, there are other direct and indirect challenges you may have faced because of the Swipe-in/Swipe-out latency issues.

Direct

Indirect

Leadership lacks good insights into all the data that was being collected through the sensors.

Unable to leverage the data to help with the betterment of the staff and provide a proactive level of security to female employees.

Solution Steps Taken To Record Real-Time Data

We helped the client process all swipe-in and swipe-out records in real-time using the AWS services. The code for the solution is designed keeping in mind the best practices of product development, so that it is easily packaged and distributed to its customers, thereby increasing their business while using the same solution internally.

The solution was built to provide the following

Near real-time door swipe in/out data to be available to any employee on customer internal portals and mobile devices for Employee Time on Floor (ToF) and Employee Time in Campus (TIC).
Maximum delay of 3-4 minutes from swipe to generate ToF and TIC reports.
Report displaying within 200 Milliseconds of request.
Processing of 1.5 million swipes a day across 8000 controllers located across India.
Allowing Employees to manually override/edit Time in Campus timings.
Allowed employees to edit the last 14 days’ TIC.
Visibility of 3 months of data (not real-time but on-demand).
Instant notifications to managers and HR updating if,
A female employee is working alone on the floor
A female employee is working alone but will be joined by ‘N’ employee(s)
A female employee is leaving late or the Swipe IN/OUT card reader is not working.
AWS QuickSight dashboards that provide the following intelligence:
Female employee shift trend analytics
Employees having low/high floor time
Number of times employees go out of the floor on an average
Average time spent by the employee out of the floor Number of employees expected on campus over holiday/weekends

Swipe-in/swipe-out Latency Issues Solution Highlights

A highly scalable and flexible solution using AWS Managed Services that provides faster ingestion and processing times of large volumes of data along with easy and secure integration capabilities with existing customer applications.
REAN Cloud design not just solved the immediate real-time availability of Tof and TiC but also included Machine Learning capabilities into the solution to provide additional reports such – as alone women on the floor, holiday schedules, etc.
The system was improved to provide the employees with an option to quickly make edits to their erroneous attendance records due to tail-gating or malfunctioned sensors.
Optimizations in infrastructure costs were observed as the solution leveraged the use of serverless microservices technology along with AWS Managed Services for all components which are billed in a pay-as-you-go model.

You might agree that we at AAIC not just solved the problem but also optimized the infrastructure cost using the serverless microservices technology. Our AWS experts know managed services well and how they are going to impact your processes.

Do you have any queries? Talk to our experts!

Establish the best security practices with our AWS expertiseScan your system and Log4j Vulnerabilities with us

Tue, 07 Jun 2022 06:42:27 +0000

Till now you must have heard everything about Log4j vulnerabilities. When the IT space was high with this, we helped our customers to overcome the fear of this threat with AWS Inspector.

Our client is a logistics platform company. They offer easy-to-use apps to help contractors, dump truck owners, and material producers in providing exceptional services with full visibility and more efficiency. Their logistic platform simplifies retail and supply chain across the globe.

In this blog, we will explain how AAIC helped the customer in overcoming the Log4j threat within a quick span of time.

The tension building

We have been supporting the client with DevOps services to transform their software delivery pipeline.

The client’s app was using Log4J – the java library. As soon as the Log4j threat was detected and declared as the worst vulnerability in 10 years by Apache, the client got scared of the same and contacted our dedicated AWS security team.

As a leading AWS partner, we help address and solve all cloud and security issues.

We deployed our AWS security experts to resolve the issues asap.

The key issues we found were:

Out of 60 instances, 19 instances were vulnerable
Possibilities of disclosure of sensitive information
Possibilities of addition or modification of data, or Denial of Service (DoS)

Want to enhance your security? Let’s talk.

How have we mitigated the problems?

We provided the following solutions to mitigate the Log4j threats:

AWS Web Application Firewall (WAF)

The first thing we did was check the WAF and set up two rules, i.e. AWS Managed Rules (AMR). These rules offer protection against malicious activities.

We set AWSManagedRulesKnownBadInputsRuleSet to inspect the request body and commonly used headers.
We set AWSManagedRulesAnonymousIpList to block requests from services that allow the confusion of viewer identity.

AWS Inspector

We ran an assessment with AWS Inspector to scan the Log4j library.
The assessment included 60 instances and was tested against 2 Rules Packages.
We checked:
Common Vulnerabilities and Exposures
Security Best Practices
Checked whether the EC2 instances in the client’s app are exposed to Common Vulnerabilities and Exposures (CVEs).
Checked whether the client’s systems are configured securely.
Checked 60 instances along with the vulnerability of the images.

Looking for AWS consultation and services? Get help now.

Log4j Upgrade

The version of our client’s Log4j library was 2.14.1 which was vulnerable. Hence, our team of AWS security experts upgraded the same to the latest and safe version.

ECR – Patch Container Images

We scanned the ECR which is an AWS managed container image registry service. Our team found 10 vulnerabilities in the client’s repository. Hence, we upgrade the images with the right versions and push them to the repository. The AWS inspector checked and verified the same for any threat possibility.

Restore your security best practices with us

At AAIC, we help businesses to set up the security best practices to restore business continuity with our AWS security expertise.

We are automation experts, with an AWS-certified team comprising 60% of our workforce. We assist you in applying intelligence to cloud and DevOps, as our name suggests.

Our AWS certified experts create high-performing cloud apps by utilizing intelligent components and smart integrations to accelerate your digital transformation journey.

Want to know more? Book a free consultation call.

Transformed AI-based Assistant with AWS Lambda & sentiment analysis

Tue, 07 Jun 2022 06:30:05 +0000

One of our customers called 7Targets offers AI Sales Assistant with lead nurturing and email writing capabilities. These sales assistants read the received responses and follow up accordingly within minutes.

Being an email-based AI Assistant, 7Targets wanted to make sure that a scalable and working solution is created to send and receive the emails. The received email is read, categorized, understood and entities extracted from. So that further action can be taken by the assistant.

But 7Targets wanted to scale the AI Assistants with the following abilities:

To send email evenly distributed over time to avoid sudden spike.
To read the email received and decide the next action to be taken by the ‘AI Sales Assistant’ after proper categorization of the received email.
To extract entities like date, phone number, title, etc from the email received.
To Set up a pipeline for continuous deployment with proper testing in the pipeline.

This is how we offered the following solutions to enhance the abilities of the AI Assistants:

Created the step functions (state machines process-lead, process-lead-response, etc.) with various lambda functions for each step.
Where the received email is accepted in the s3 bucket using SES.
Process the received response and categorize based on sentiment and certain properties available in the response like date, etc.
Handle any bounce and complaint email responses.

This has helped 7Targets to solve the problem of sending the email and then the follow-up email. The right categorization of the received email, based on which lead state was updated and the action ahead was decided by a state machine.

Along with categorization from the email text, there was also a need for entity detection like date, phone number, etc. which was done as part of this implementation. For example, if an email was received and categorized to be ‘Out of Office’ then the return date was identified from the email so that the assistant can follow up after the leads return to the office instead of following up in the Out of office period.

We used the following third-party applications:

Debounce and Zerobounce APIs are used to validate the emails of each lead. This is the first step in the process-lead step function
3rd party CRM like Hubspot, ZohoCRM is used and the 7Targets AI Assistant solution interacts with that solution

With our solutions, 7Targets achieved the following results:

Able to grow per day email sending from 1K to like 10K and per day and can still grow more with the present solution.
Able to process the response at a similar level but the responses are not as high as email sending.
Will proper limits and thresholds be set and with continuous monitoring of failure for lambda and step functions the limits and thresholds are changed.
The container in Fargate is auto-configured to scale up and down.
The Continuous deployment pipelines are helping with automated zero-downtime deployments to QA and then production setup

Empower your chatbot with us

At AAIC, we are instrumental in offering AI-powered automation inclined with AWS cloud services. Our AWS certified team closely sits with you to understand all your requirements to offer you the best solutions. We make your solutions scalable and affordable. And also with us, you can significantly reduce your software development time.

We used the following technologies to help 7Targets:

AWS Stepfunctions, AWS Lambda, AWS RDS, AWS Comprehend, AWS S3, AWS SES, AWS ECR, AWS Fargate, AWS SNS, AWS SQS, AWS API Gateway, AWS Cloudwatch, AWS ELB, AWS Cognito, AWS WAF, AWS CloudFormation, AWS DMS, AWS Route53

Need AWS consultation? Book a free consultation call.

Are Your Microservices Not Well-Managed?

Wed, 01 Jun 2022 07:20:09 +0000

In the past few years, many companies have shifted from a monolithic to a microservices approach. However, as with any approach to application development, the microservices architecture has its own challenges.

There are several use cases where organizations noticed an increased complexity and effort to develop applications with a microservice framework.

This is what happened to one of our clients too!

Being a leading SaaS-based company in Sales and marketing, they wanted to do upgrades at will along with predictability.

And because their existing microservices were not well-managed in terms of deploying only the updated ones. They faced subsequent other problems too.

Want to scale your business? Let’s connect!

Challenges you Face When Your Microservices are not Well-Managed

A small change of code would result in the deployment of almost all the services.
Any modification had a significant impact, and even though the process is automated, testing may take a long time.
Instead of click-button automation, upgrading is an activity itself that requires the entire team to collaborate.

How To Manage Poorly-Managed Microservices

The first step to solving any problem is to understand the situation well. The causes and the effects. This is why our automation experts first engaged with your team to understand the challenges & requirements.

Then, after understanding the challenges, the code structure, and the different microservices we take these key steps:

Restructuring the code to differentiate the services into separate folders and the common dependent code into one.
Creating pipelines for each of the services with the proper code component dependencies defined in the pipelines.
Separating the GitLab repository for each service to provide ease of maintenance for their small engineering team.
Assisting you in developing new export microservices as per the requirement.
Ensuring the proper usage of serverless and EKS with the right configuration for some services running in JRE in a docker container ensures zero downtime upgrade.
Providing the proper README and other details makes it easy for the customer to maintain all the services well in this structure.

To help our readers better understand the solution, here is the graphical presentation of our DevOps pipeline for serverless microservices and zero downtime upgrades.

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DevOps Pipeline For Serverless Microservices and zero Downtime Upgrade

Technology Stack For Serverless Microservices & Zero Downtime Upgrade

Serverless.io

Step functions

EKS

Dynamo

SNS

SQS

Lambda Functions

GitLab CI/CD

RDS

As you might have realize that adopting a certain technology is not enough. You need to understand your requirement, environment and the right technique to implement the solution. This is where AAIC team bring their expertise and help you get the scalable solution.

As in above example, we not only resolved their poorly managed microservices but also assisted them in achieving zero downtime upgrades.

Want to know more? Book a free 30 minutes consultation call

Release 46% More Features with Mature Deployment Pipeline.

Wed, 01 Jun 2022 06:55:43 +0000

Product managers are always looking for ways to improve their products, optimize the user experience, make the product more valuable, and therefore increase user engagement.

But, how often do they get success?

Well, it depends on the speed of feature releases.

Let’s say, you build an app and release the beta version for public testing and feedback.

And Feedback came.

Your users showed that specific features needed to be added, modified, etc.

Now, what will happen if you lag or delay the release of the required features?

Well, your users will switch to your competitor who not just listens to the feedback but also releases the required features at a faster pace.

Don’t worry! Here we will demonstrate how you can release 46% more features with a mature deployment pipeline and beat the competition.

Tired of manual testing costs? Talk to our experts

Why You Fail To Release Features When Required

Let’s understand this with a real example. One of our clients had three environments to manage: development, quality assurance, and production.

Every day they were making small changes and moving them to the testing environment. It was then moved to production if everything went well.

And because there was no automation in place, they were facing the following challenges.

So you got it! Right?

Now, let’s move toward the solution.

Steps To Release 46% more Features with a Mature Deployment Pipeline

Follow the steps below to do the frequent releases without deployment error.

Add pipelines for automated releases.
Include multiple stages in pipelines like quality checks, automated tests, vulnerability checks, etc.
Add parallel and automated deployment.

Continuing the above example of a client experiencing deployment challenges, here is a closer look at the steps we took:

Assisted in performing automated releases with pipelines that included quality tests, dependency, vulnerability, static code analysis, and deploy production stages.
Provided a PR pipeline and environment-specific deployment pipeline for 10+ microservices.

We designed it in such a way that all of their code even though It is in a single repository for

different services the right service is deployed as per the code change. Along with

deployment to the white-listed setups.

This type of individual service build and deployment was possible with the right design and

configuration of various dependencies each service has on different code components in the repository.

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Technologies we Used in the Mature Deployment Pipeline

Connect with Top AWS Professionals

DevOps Pipeline For Faster Releases

To help you better understand the process steps, here goes a graphical look at the DevOps pipeline for faster releases.

The competitive market demands faster releases not only for startups but also for established players. As you could see, how we helped the leading sales company in making faster releases without error. That ultimately helped them build the authority in the market.

Would you like to be the leader in your industry? Connect with us today!

Build Serverless Backend Solutions with AWS to Reduce your Infrastructure Cost

Tue, 31 May 2022 07:46:04 +0000

One of our customers called ClickToConsult which provides an E platform for service seekers to access quality professionals & consultancy via audio & video conferencing, wanted to give instant access to the services by saving time and costs. It offers a mobile application, which is available in the App Store and Play Store.

Want to build a mobile app? Let’s talk.

As the client wanted to scale, we helped them to build a web-based interface to manage administration activities.

We hosted the backend in AWS serverless lambda. Lambda functions work on-demand when there is a request to the API gateway. And when there is no request to serve, lambda functions will shut down automatically – which saves the cost

Lambda functions can also scale on demand. So in future when there is a load on the system, it can easily scale-up up to a certain threshold.

Wondering about AWS? Talk to our expert.

With this, the consultants can create their profile and users can view the consultant profile using the platform to book an appointment. We also integrated the app with the third party Razorpay to handle all the transactional requests.

The service providers can use Razorpay as the payment gateway to handle the transactions. Firebase is used to send push notifications to android and iOS platforms.

If you have any questions, book a free consultation with us.

We used to following technologies to deliver the solutions:

AWS Step Functions
AWS Lambda
AWS RDS
AWS S3
AWS SES
AWS ECR
AWS Cloudfront
AWS SNS
AWS SQS
AWS API Gateway
AWS Cloudwatch
AWS ELB
AWS Cognito
AWS WAF
AWS CloudFormation
AWS Route53

With our solutions, ClickToConsult is offering consultancy across India and the middle east

from their home or office. The serverless solution helped the client to save the cost as developers are only charged for the server space they used and no cost involved in managing the infrastructure.

Read the related case study here.

Enhance your day-to-day operations through Amazon workspaces and IaC

Tue, 31 May 2022 07:37:23 +0000

How many businesses go down for a non-scalable environment and increasing costs?

Automating manual infrastructure is one of the biggest challenges businesses face today.

With the rise of ‘Infrastructure as Code (IaC)’, there is a huge rush to automate the infrastructure and run as IaaC.

Manual infrastructure deployment results in errors and it is time-consuming.

We interact with many CEOs regularly and they have mentioned some of the issues they are:

Lack of expertise in using AWS services resulted in doubtful decisions.
Non-scalable environment
Manual deployment of AWS infrastructure and application deployment for each new environment was time taking and error-prone.
Some packages fail to install on EC2 servers for unknown reasons.
Lack of a sound development environment.

Automating the AWS deployment architecture using Terraform code can scale the infrastructure.

Want to automate? Let’s talk.

And the benefits of deploying Infrastructure as Code (IaC) are:

Decreased risks

When you provision all your infrastructure manually with one person and when this person leaves your company, there is a chance that this person may take all knowledge to another company.

When you implement IaC, it minimizes all such risks. IaC empowers you with infrastructure as a reproducible block of code with less error.

Such infrastructure resides in a source code repository. Any change and history are visible to all.

Stable environments for faster iterations

Usually, manually configured environments slow down your product development. This happens when you want to change the architecture of the product to enhance customer satisfaction.

When you have IaaC, your environment is stable, easy to modify, and consistent. Your infrastructure lives as code along with your product. Such harmony helps you to develop new features for the product faster.

Wish to have a stable environment? We have solutions for you.

Cost optimization

When your entire infrastructure is visible you can see what is running and what is not. You can easily optimize your costs to maintain profit margins. Such optimization is so easy with IaaC.

Self-documenting

Infrastructure as Code is self-documenting in the sense that when a new developer joins your organization, he/she can understand the logic just by reading it. With this, you can easily add more people to your team. It significantly reduces your onboarding time to your project.

In addition to IaaC, the following solutions can be beneficial too:

Installing and configuring the servers using Ansible.

Providing configured Amazon workspaces for their day-to-day operations.
Guidance to use Global Accelerator.
Use Gitlab for Issue tracking, Continuous integration, and Continuous deployment pipeline features.

Bring organization-wide automation with us.

Why choose us?

At AAIC, we help you build better products faster. As a Cloud System Integrator (CSI) and DevOps Service Provider, we enable enterprises to transform securely by accelerating the migration of their application and big-data workloads to the cloud with improved governance.

Offer fully automated Terraform code for development and production environment within a month.
CI/CD pipeline for automated deployment.
Create Ansible automation for EC2 configuration within 5 days.
Enhance your day-to-day operations through Amazon workspaces
Solve any support tasks within 24 hrs.

We also help you master performance and collaboration within your team through our specially designed DevOps Services which

Helps you decide on the CI/CD platform based on your application suitability
Quickly select the best suitable pipeline for your application
Contain easy-to-use and standard templates that can be modified to suit your unique application.
Incorporates templates based on recommended architecture.
Provides templates that have been used in production for multiple customers with varying degrees of application complexity

One of our customers specializes in messaging and mobility solutions. Their mobile solutions give customers an undisputed improvement in Visibility, Velocity, and Reach while upgrading levels of Standardization and Decentralization.

Read this case study to understand how we have automated their infrastructure with IaaC.

If you have any query, talk to our expert.

Want to Reduce Operational Cost By 70%? Time To Migrate From Rundeck To AWS Batch

Fri, 27 May 2022 09:33:05 +0000

Delays in feedback not only raise the cost of fixing problems but also result in various additional issues.

Hence to make things faster companies are adopting automation for different tasks.

Rundeck and AWS Batch both serve the same purpose of automating and making things faster and easier. But they are different in terms of adding value.

Rundeck is a runbook automation platform that is used to make common operations more self-service for others in an organization. To understand this,

Assume you’re a software engineer who’s built a script to clear a cache, restart a service, or run a data processing job. If you or your team are the only ones with the permissions and experience to securely run that script, you may soon become a constraint for others in the company that require that task.

Rundeck allows you to convert this script into a GUI-based internal tool that you can share with others. Anyone with the appropriate rights can then run the script themselves, even if they lack the knowledge or access to do so.

Want to automate Testing? Try AITest!

However, there are some aspects of Rundeck that make it difficult to use and expensive.

Let me tell you the challenges of one of our clients who operated in a marketing & advertising agency in the USA and How Migrating to AWS Batch helped them save 70% of their operational cost.

Challenges of a Content-Based Marketing Business with Rundeck Platform

Being a content marketing platform, they target helping companies that wish to produce and expand their own content for the purpose of new customer acquisition. But they were facing the following challenges:

Once they figured out their problem, they started looking for the right solution. This is where AAIC came into its visibility. With over 60+ percentage of AWS certified professionals and work history, they knew we can bring the optimal solution.

Solution Steps To Reduce the High Operational Cost and Visibility Issue

After looking into their high operational cost and visibility issue, we knew they needed to migrate to AWS Batch service in place of Rundeck.

For a quick intro, AWS Batch has its own console where on the dashboard we get all the details of the jobs that are scheduled.

As per our suggestion, they migrated to AWS batch and further our AWS Experts:

Composed it in 5 spot EC2 instances to run the batch jobs.
Configured and stored all the configurations and secrets in AWS Secrets Manager.
Used AWS Cloudwatch event rules to schedule the jobs.

All the batch jobs were deployed in a production environment using terraform automation. This helped manage the entire job scheduling and configuration as a code.

This is how we helped them fix failures and errors quickly and redeploy them with a click.

Common Technology Stack Used For Rundeck To AWS Batch Migration

Need AWS Professionals? Hire experts

Key Aspects while migrating From Rundeck To AWS Batch

Understand the existing jobs, frequency, and dependencies.

Job Customization to make work with AWS Batch.

POC of AWS Batch

Scalability & Performance

Apart from the AWS-specific solution, we have our own accelerators, which include marXeed, DevOps-in-a-box, and AITest, that allow our customers to realize the value in days or weeks.

Want to know more? Book a free 30 minutes consultation call

How To Achieve Faster Releases and Scalability through DevOps Pipeline

Fri, 27 May 2022 09:17:18 +0000

Today, every VP of engineering looks for ways to make continuous & faster releases to production!

Why not? It makes a huge difference to one’s rate of success.

What if I tell you that selecting the right partner can make it easy for you.

Being the right partner, we help businesses like yours make faster releases of new or revised software features every single time.

How?

Through our specially designed DevOps pipeline and Infrastructure as a code.

Tired of manual testing costs? Talk to our experts

Although, We have already helped many companies in our journey so far.

Today we are going to share a story of a USA-based fintech startup, its challenges, and the solution steps we took. Proceeding ahead you will also see the architecture diagram along with the technologies we used.

So, stay tuned!

Key Challenges a Fintech Startup Faces

With the rise in fintech startups, funding, and the urgency to deliver fast, 2 key challenges are always there:

Solution-Steps for Slower Releases and Scalability Challenge

While having any solution it’s important to have a one-to-one discussion with the engineering and product development team. Understand all the upfront challenges and then make a sketch plan of solutions.

Keeping in mind the above 2 key challenges, our solution majorly includes these key steps:

A Pipeline design for their frontend and backend while integrating Github so that they can keep track of all the changes that have been pushed to the repository.
Creation of Infrastructure as a code to help startups automate infrastructure provisioning allowing them to develop, deploy and scale with less risk and low cost.

Now let’s have a look at how our CI/CD and backend pipeline helps the fintech startup to have a scalable solution:

Technology we Used To Make Faster Releases For Fintech Startup

Need AWS Professionals? Hire experts

Now, wrapping up let’s look at the key aspects we focused on while delivering a scalable solution to a Fintech Startup:

Apart from the fintech-specific solution, we help companies master performance and collaboration within their team through our specially designed DevOps services.

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Proven tips to bridge both digital and cash payment platforms with a hybrid app

Fri, 27 May 2022 08:05:25 +0000

How many times do you receive candies instead of changes in any retail shop?

If you don’t have a sweet tooth, it is irritating! Isn’t it?

Well, it is difficult to manage leftover coins and bills in a physical payment environment.

Like this only, one of our customers was finding it difficult to manage leftover coins and bills. With this, customer satisfaction was declining. Hence, the client was looking for a digital payment solution that can make the best use of the leftover coins.

Building a scalable and secured payment app isn’t easy! Here are some tips that might come in handy for you.

Need more help? Book a free consultation call

Develop the Backend APIs

First, you can build the backend APIs for all the back-end services from the server side. You can use NodeJS for writing the APIs and to serve the demands of the app, admin portal, and POS system.

Develop the Frontend Application Framework

You have to develop the mobile app for Android and iOS. And for this, you can use Flutter to develop a cross-platform mobile app faster. Flutter empowers you to develop apps for Android and iOS using the same code. It can save you precious time!

Integrate your app with the POS App

It’s time to integrate the application with the POS app in native Android technology to avoid any unknown system-specific issues.

Hire the best AWS team

Integrate a payment gateway

Now you can integrate a payment gateway with the app to store and manage the user’s bank accounts with security. It can help you to get away with compliance-related issues.

Integrate an SMS gateway

Your team can integrate an SMS gateway service now. With this, the end-users can get OTP SMS. This comes with scalability. Depending on the increasing user base, you can accommodate more numbers by scaling the OTP service. In addition, you can also integrate a cloud messaging service for push notifications.

Deploy AWS ECS

You must use Amazon Elastic Container Service (AWS ECS) to build the server of the app. The auto-scaling and CI/CD allow you to grow as per demands and release apps faster than before. It allows you to rent VMs as per the need of your app and traffic.

Download the full case study

Build a hybrid payment app from scratch with us

We at AAIC are instrumental in building payment apps with optimum security. Our team of certified AWS architects and engineers can digitize your whole infrastructure to accelerate your revenue and customer satisfaction. Everything we do right from planning to execution, we follow all AWS technologies along with a hyper-automation approach to incline the right cloud strategy.

If you have any more questions, talk to our experts.

How to boost deployment success rate with Python Selenium Automation?

Tue, 24 May 2022 09:43:15 +0000

Many organizations need a significant amount of time and effort to test their applications.

Additionally, before deploying the upgrade to production, the possibility of breaking the program’s working functionality due to closely coupled application dependencies is always a major concern.

You can mitigate these problems by hiring a big manual testing team. But it takes days and weeks to get the app into production.

Additionally, it costs a lot!

Tired of manual testing costs? Talk to our experts

Hence there are some tactics we implemented for one of our customers to resolve the above-mentioned issues:

We developed the test cases for different levels like execution, report generation, and so on.
We used Python (pyTest framework for API testing) and a Selenium-based framework to
automate the user interface.
We also integrated the automation suite into the CI/CD pipeline to ensure that it runs for every new deployment across all environments.
For the test scenarios, We created Allure reports so that we could track their success or failure.
To achieve the required agility, we built a pipeline dedicated to running Selenium automation tests for web application testing. We utilized the allure tool (a versatile lightweight multi-language test report tool) in addition to selenium to create the test report, which can be stored and hosted on AWS S3.

Our AWS experts can help you

The Selenium Test Automation Pipeline, that we implemented, can be visualized in the diagram below:

As a Cloud System Integrator (CSI) and DevOps Service Provider, we enabled these enterprises to transform securely by accelerating the migration of their application and big-data workloads to the cloud with improved governance.

We helped the client to:

Decide the CI/CD platform based on your application suitability
Quickly select the best suitable pipeline for your application
Quality and security compliances baked in as part of pipeline automation
Incorporates templates based on recommended architecture.
Provides templates that have been used in production for multiple customers with varying degrees of application complexity

Want to know more?

Talk to our DevOps experts

Published By

Manan Tank

Cloud Champion

Manan Tank is a passionate cloud expert. He is a fast learner and contributes to our major projects. In his free time, he loves to travel and do road trips.

Step-by-step guide to create an autoscaled GitLab runner on AWS

Thu, 19 May 2022 10:15:56 +0000

Businesses often strive to autoscale despite being on the cloud. There are many underlying issues that create hindrance in autoscaling.

And sometimes, it’s become difficult to get to the roots of such issues.

Exact same thing happened with one of our customers.

Like most businesses, 7Targets is also having their code repositories in GitLab and using GCP instances as a GitLab runner.

Continuous Integration and Continuous Deployment won’t work without a GitLab runner. GitLab Runner is an application that works with GitLab CI/CD to run jobs in a pipeline.

But, due to some reasons, the runner instance’s availability zone was down. So, our customer was not able to push any changes because without a runner job won’t start.

I suggested the client create an autoscaled GitLab runner on AWS. So, in future, any of the cloud providers instance’s availability zone goes down, it won’t affect the work going on.

Below are the steps I followed to create an autoscaled GitLab CI/CD runner:

The first step is to install GitLab Runner in the EC2 instance, which will act as the runner manager which spawns the new machines. Choose a distribution that supports both Docker and GitLab Runner, such as Amazon Linux, Ubuntu, Debian, CentOS, etc.
You can choose any small instance type such as t2.micro as it will not run any tasks by itself. This machine will be a dedicated host as we always need it up, thus it will be the only standard cost.
Install the prerequisites:

a) Connect to EC2 Instance: b) Add official GitLab repository:

For Debian/Ubuntu/Mint:

curl -L "https://packages.gitLab.com/install/repositories/runner/gitLab-runner/script.deb.sh" | sudo bash

For RHEL/CentOS/Fedora:

curl -L "https://packages.gitLab.com/install/repositories/runner/gitLab-runner/script.rpm.sh" | sudo bash

Install GitLab Runner from the GitLab repository by entering the below cmds.

For Debian/Ubuntu/Mint:

## sudo apt-get install gitlab-runner

For Debian/Ubuntu/Mint:

## sudo yum install -y gitlab-runner

Want to know more?

Let’s discuss.

c) Install Docker

For Debian/Ubuntu/Mint:

## sudo apt-get install docker

For RHEL/CentOS/Fedora:

## sudo yum install docker

d) Register the Runner

Run the following command:

## sudo gitlab-runner register

Enter your GitLab instance URL.
Enter the token received to register the runner. (As shown in below image, You will find GitLab Instance URL and registration token in Project Group Settings > CI/CD>Runners)

Enter the details for the runner. You can change this value later in the GitLab UI (User Interface).
Enter tag associated with runner, separated by comma. You can change this value later in the GitLab UI.
Enter a maintenance note for the runner(optional).
Enter the runner executor.
If you enter docker or docker+machine as executor, you have to enter the default image to be used for the projects that don’t define the image in .GitLab-ci.yml.

Configuring the Runner

Now that runner is registered, you need to edit its configuration file and add the necessary options for the AWS machine driver.

Configuration file path: /etc/gitlab-runner/config.toml

In the global section, you can define the limit of jobs that can be run simultaneously across all runners (concurrent). This steadily depends on your needs, like how many users GitLab Runner will consider, how much time your builds take, etc. You can start with a low value like 10, and increase or decrease the value going forward.The check_interval option means how often the runner should check GitLab for new jobs (in seconds).
From the [[runners]] section, the principal part is the executor which needs to be set to docker+machine. Most of those configurations are taken care of when you register the runner for the first time.
In the [runners.docker] section you can specify the default Docker image used by child runners if it’s not specified in .GitLab-ci.yml. Using privileged = true, all runners will be able to run Docker in Docker which is useful if you want to build your own custom Docker images via GitLab CI/CD.
Add the “/var/run/docker.sock:/var/run/docker.sock” to the array of volumes. Docker socket file is located at /var/run/docker.sock and it is used to communicate with the main docker daemon process by default. It is the entry point for a Docker API. By default, This socket is used by Docker CLI to execute docker commands. Need more help?
[runners.machine] is the most important part of the configuration and it’s the one that tells GitLab Runner how and when to spawn new or remove old Docker Machine instances.
Enter the Docker Machine driver, here it is set to amazonec2 and the machine name has a standard prefix followed by %s (required) that will be replaced by the ID of child runner: GitLab-docker-machine-%s.Now, based on your AWS infrastructure, there are a number of options that you can set up under MachineOptions. Here, you can see the most common options.
In [[runners.machine.autoscaling]] section you can define the following:
IdleCount means number of machines that need to be created and waiting in Idle state. At the beginning, when no jobs are queued, GitLab Runner starts two machines (If you give IdleCount = 2), and sets them in Idle state.
If IdleTime is set to 0, the autoscaled runners are terminated just after finishing the job.
IdleTime means Time (in seconds) for a machine to be in Idle state before it is removed. In case of IdleCount=60, wait for 60 seconds before actually terminating the runner.

In GitLab, You will get the runner details in Project Settings > CI/CD > Runners (As shown in the below image)

Autoscale with us

We at AAIC, help businesses with cloud and DevOps services to boost your time to market. With a team of AWS certified engineers and architects along with our DevOps experts, we support you to plan and implement a cloud strategy in line with an agile approach. With us, you can push your apps to the market faster.

Driven by automation, we help customers to improve their software pipeline and on-premise infrastructure with cloud and DevOps.

Want to know more? Book a free consultation call.

Let us help you to adopt a DevOps culture

Published By

Manan Tank

Cloud Champion

Manan Tank is a passionate cloud expert. He is a fast learner and contributes to our major projects. In his free time, he loves to travel and do road trips.

Top 6 secrets to build a DevOps culture

Wed, 18 May 2022 10:40:55 +0000

I talk to many CEOs regularly. They are struggling with a long go to market duration, frequent feature releases, fixing issues in the production faster, and scaling the products.

In this blog, I am discussing how adopting DevOps can help you overcome these issues.

I am also highlighting how to nurture your organizations to adopt a DevOps culture.

INSPIRE THE CONTINUOUS IMPROVEMENT

To nurture a DevOps culture, as a leader you must inspire your team to use continuous improvement.

DevOps brings end-to-end responsibility, which brings constant changes to any organization.

Businesses must practice adaptability to the changes whether it is new customer needs, new technologies, or legislation changes.

The culture of adopting continuous improvement helps your team to get used to the CI/CD pipeline, which is an integral part of DevOps.

Want to know more? Talk to us.

FOCUS ON CUSTOMER NEEDS

When you start focussing on fulfilling customer needs, it’s become easy to nurture a DevOps culture in your organization.

With DevOps, you get data. The automated processes always create data and you should constantly review all the data to ensure that performance targets are met.

But you should focus on the right data, such as the duration between the initial code commit and changes in the production.

Also, you must check the error rate while you deploy new software in the production environment and the meantime recover during any service interruption.

However, always focus on the matrices that matter to your customers.

END-TO-END ACCOUNTABILITY

When you look at the conventional software development model, you can see that the developers and operations play separate roles.

But DevOps makes both these groups work together as a team, which is fully responsible for the end-to-end deployment of the apps.

The core pillar of DevOps is to control and take responsibility for services from “concept to grave.”

I think the gap between developers writing code and the operation team deploying the same is full of inefficiencies. These can occur from differences in production, performance, and unpredictable environments.

HYBRID IT AUTOMATION

How much time do you need to respond to your customer feedback?

You must automate your process to strive for continuous improvement.

It speeds up your cycle time. Businesses are experiencing notable development in automated tools to integrate processes including the CI/CD pipeline.

You must automate your

Infrastructure provisioning
Building new software
Deploying software and new system
All tests from functionality to security compliance

In addition, your DevOps team can create indigenous automated processes. You can train your system to deploy apps faster with more reliability.

Want to know how to synchronize your team with automation?

NURTURE A COLLABORATIVE ENVIRONMENT

I think that the prime motive behind DevOps is to combine both development and operations to build a unilateral team driven by common objectives.

Brands need to inspire their development and operations teams to communicate regularly, share ideas, and resolve issues together to achieve this.

I think breaking down silos is essential to bring both these branches together.

Businesses need to align people, processes, and tools toward customer success.

This happens only when you, as a CEO or CTO, try to nurture a collaborative culture together.

When you shift your siloed working culture towards a collaborative environment, your business faces a cultural shift and it should follow a top-down approach.

Hence, getting the right people who are confident enough for this cultural shift is essential.

EMBRACE FAILURE AND FAIL TEST

Gone are the days when you keep on measuring your team’s effectiveness based on their success.

As per my understanding, DevOps culture needs a fail-fast approach to grow. It is just the opposite way of measuring success based on efficiency.

Let me tell you why.

The faster you fail, the faster you discover the successful way to do it. The core idea behind this is not to maximize failure but to inspire your development team to experiment with failure in a structured environment.

The faster they fail, the quicker they can discover ways to enhance the apps. Originally the fail-fast approach is popular in agile methodology.

But you can migrate it into DevOps also. But here the managers need to control the costs of failure, especially when it affects your customer services.

If you allow your developers to fail early during the development process, they can detect the defects and errors before the same goes for deployment.

HOW CAN AAIC HELP YOU?

At AAIC we are automation-driven. With the support of excellent DevOps and AWS certified teams, we are accelerating your time to market. With us, you can develop and deploy your apps faster. We have been working with top American companies to speed up their development cycle.

We also understand how critical it is to build best the DevOps practices with the right cloud alignment.

To know more, book a free consultation call.

AAIC is technology services company that provide high-end engineering services and is proficient in enterprise class for IT-delivery.AAIC focus on Automation in Cloud and DevOps

Top 9 AWS technologies you need to modernize a CMS

Wed, 18 May 2022 09:56:34 +0000

Security is the biggest issue any businesses face with legacy applications. Along with high-security vulnerability, slow development cycle, and app crashes are other major issues you face when you don’t modernize your legacy apps.

One of our customers called EMFI faced similar challenges and we planned to modernize their legacy CMS with AWS technologies along with DevOps.

In this post, we will discuss how we modernized the CMS app of the client.

CONTAINERIZATION

We proposed to containerize the EMFI app using the AWS ECS Fargate service. It helped the customer to package the software code together with their related libraries, frameworks, and other dependencies.

It became easy for the customer to move and run the isolated containers in any OS and environment. With this, EMFI could easily fix bugs and glitches as containerization made the code independent from the surrounding environment.

Curious about how containerization works? Talk to our experts.

CI/CD IMPLEMENTATION

The process of shipping code was difficult. It took weeks to finish a release cycle. Simple issue fixes took two weeks to make it to production, and the releases themselves were brittle and prone to new bugs. Furthermore, the manual deployment process frequently failed.

Hence, we implemented CI/CD for EMFI apps by accelerating their software release cycle.

TRAFFIC MANAGEMENT

We set up Application Load Balancer which distributes the incoming traffic across containers. Upon distributing the traffic in the containers, it automatically scaled the workloads. Also, we deployed a monitoring dashboard on the datalog.

Need autoscaling during peak time? Let’s talk.

CLOUD MIGRATION

EMFI kept their apps on private GitLab and we migrated the same into public SaaS GitHub. We also implemented an auto-scaling group comprising a collection of EC2 instances that are combined as a logical group. So, it becomes easy to scale the same with features including health check replacement and scaling policies.

INFRASTRUCTURE AS CODE

We also deployed Terraform, an open-source tool for infrastructure as code. Our team managed and provisioned the data centers through machine-readable definition files to get rid of physical hardware configuration or any other interactive configuration tools.

Curious? Let our experts help you

ECS

Amazon Elastic Container Service (Amazon ECS) offers businesses a highly scalable and quick container management service. It enables businesses to easily run, stop, and manage containers on a cluster. It is also a fully managed container orchestration service. With its deep integration with the rest of the services, it is one of the most popular solutions to run your container workload anywhere.

ECS CLUSTER

An Amazon ECS cluster is a regional grouping of one or more container instances on which you can run task requests. It can be a logical group of capacity providers or container instances. If you run services or tasks that use EC2 launch type, the ECS cluster is a group of container instances and if you use the capacity providers, an ECS cluster is a capacity provider.

ECS TASK DEFINITION

We used task definition to run the docker containers that are used to run the application. You can specify various containers in a task definition. There are task definition parameters and not all parameters are valid. And the parameters vary as per the launch types.

EC2 stands for Elastic Compute Cloud. It is nothing but a basic computer virtually present in the AWS cloud which you can access and do whatever you can do on a basic computer.

EC2 INSTANCE AND ECS SERVICES

ECS services define the minimum and the maximum tasks that are running from a single task definition at any given point.

VPC

We implemented a virtual private cloud (VPC), which is a secure, isolated private cloud hosted within a public cloud. Along with this, we implemented an application load balancer that allowed a developer to configure and route incoming end-user traffic to applications based in the AWS public cloud.

IAM AND RDS

Our team also deployed AWS Identity and Access Management (IAM) to enable EMFI to manage access to AWS services and resources securely.

Apart from this, we facilitated Amazon Relational Database Service (Amazon RDS) which made it easy to set up, operate, and scale a relational database in the cloud.

MODERNIZE YOUR APPS WITH US

AAIC is an advanced AWS partner with DevOps expertise. Deeply inspired by automation and AI, we at AAIC have been transforming many organizations to leverage cloud and DevOps with our impeccable expertise. Our AWS and DevOps engineers directly consult you to understand your legacy apps and advise changes accordingly. With us, reduce your time to market and get data security with advanced cloud solutions.

Want to know more? Book a free consultation call

AAIC is technology services company that provide high-end engineering services and is proficient in enterprise class for IT-delivery.AAIC focus on Automation in Cloud and DevOps

AAIC helps Skyword for Migration and Modernizing Application Deployment and Maintenance With AWS.

Tue, 26 Apr 2022 10:55:52 +0000

PROJECT GOAL

AAIC helped Skyword to achieve application migration and modernization with the existing team. This also reduces the efforts on deploying, maintaining and supporting the existing applications. Skyword also achieved an optimal automation and DevOps on Skyword 360.

BUILDING THE SOLUTION

AAIC engineers team worked with Skyword technical leadership and Product owner team to develop modernized business solution which includes.

Containerising and automating the deployment of environments for the Skyword app and migrate on K8S
CI and CD implementation for Skyword application.
By using GitHub actions for building the WAR/JAR files and automated deployment of the same on different AWS ASG. Cloud Formation templates are used for creating the infrastructure. Selenium testing framework is integrated with CI pipeline for functional testing. OWASP is being used for security/DevSecOps. CloudWatch metrics and logs are used for monitoring the system. Lambda functions are written to scale up/down the environment from cost saving perspective.
API and Selenium UI automation testing of Skyword application use cases, and make it part of the CI/CD
Setting up Operations/Monitoring dashboard like TrackMaven
Help in migrating private GitLab to public SaaS GitHub.
Customer had below mentioned challenges, so we migrated to GitHub.

Inability to host everything on gitlab.
Client team was familiar with GitHub and onboarding them on Gitlab was taking much effort.
Gitlab’s sack integration was not efficient.

ARCHITECTURE

Information on clients to be deployed securely in the Cloud. Standard 3 Tier architecture is used. Clients can login with appropriate IAM security. Group rules and security were also used.

Within AWS the architecture utilised Amazon’s Elastic Load Balancers combined with Auto Scaling Groups to balance the traffic across multiple instances and automate the creation/termination of instances across multiple Availability Zones. A number of components were also split out from the current infrastructure to make it more modular and fault-tolerant. This provides redundancy across multiple geographic location.

DEPLOYED AWS SERVICES

• AWS S3 Bucket

S3 is an object storage service. To store the images formatted by the openross application S3 bucket is getting used.

•AWS Elastic File System

EFS is a regional service storing data within and across multiple Availability Zones for high availability and durability. For sharing the file system across multiple applications amazon elastic file system is used.

•AWS Cloudwatch Log Group

Amazon CloudWatch Logs used to monitor, store, and access application logs. For storing the application logs, AWS CloudWatch log group is getting used.

•AWS Application Load Balancer

ALB is used for distributing incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones.

•AWS Route53 Hosted Zones

Route 53 is a highly available and scalable DNS web service. It is getting used for routing internet traffic to the skyword applications.

•AWS Secrets Manager

All the skyword application secrets or properties are stored in AWS Secrets Manager.

•AWS Key Management Service

AWS Key Management Service is a managed service that makes it easy for you to create and control customer master keys, the encryption keys used to encrypt your data. It is getting used to encrypt the EFS data and Skyword applications secrets.

•AWS Elasticache (Redis)

Amazon ElastiCache for Redis is a Redis-compatible in-memory data store service. It is getting used for session management in skyword web application.

•AWS Certificate Manager

•AWS System Manager Session Manager

Session Manager is a fully managed AWS Systems Manager capability that lets you manage your EC2 instances, on-premises instances, and virtual machines (VMs) through an interactive one-click browser-based shell or through the AWS CLI.

•AWS Identity and Access Management

AWS Identity and Access Management (IAM) enables you to manage access to AWS services and resources securely.

THE DATA LAYER

RDS was used for the data-layer. AWS Aurora is a MySQL-compatible RDBMS which was designed from the ground-up to run on AWS, and has a number of benefits over MySQL running on RDS which is more durable and redundant

Deployment

Application Instance creation, launch and updating is managed using AMI.(Amazon Machine Image.)In order to update AMI i.e., modifying WAR/JAR files present in the AMI, AMI bakery is used. It also provides support for deploying it into the respective auto-scaling group. AMI deployment process is done in a blue-green fashion where a new instance will be launched and only after that older or previous instance will be terminated.

AWS lambda function has been set up in environment. This lambda function is responsible for scaling up or scaling down autoscaling groups. It can be scheduled to scale up or scale down at a specific rate or crontab also.

VERSION CONTROLLING YOUR INFRASTRUCTURE

Git VCS is used to manage the versioning of the infrastructure. a git repo is created with all of the relevant setup & configuration scripts, along with any relevant documentation etc. Detailed information on how to recreate our infrastructure from scratch is combined with CloudFormation which allowed to automate the creation of almost all AWS services.

THE MIGRATION BENEFITS

Uptime Significant improvement, which can monitor with 3rd party tools and confirmed.
No single point of failure
Ability to innovate Being on AWS opens up an array of new services and technologies that are now significantly more accessible to the average IT group. Whether it’s looking at new storage engines like Redshift or services like Amazon Machine Learning or Lambda, the time to implement – and therefore innovate – is significantly reduced.
Integration to other systems Being on AWS opens up new possibilities to integrate including best of breed tools & services to connect to other systems.
Scale Elasticity, load balancing and the capability to scale automatically is a big bonus.

COST OPTIMIZATION

With the deployment and output of AWS Migration Evaluator, Skyword was able to see detailed reports of their inventory.

The utilization of resources were analyzed, and this helped in right-sizing the company’s AWS solutions. Some of skywords existing resources were over-provisioned and right-sizing their resources resulted in significant cost savings.

With the use of tagging and AWS Billing and Cost Management, an ongoing review was carried out to ensure skyword operated at cost efficiency.

AAIC is technology services company that provide high-end engineering services and is proficient in enterprise class for IT-delivery.AAIC focus on Automation in Cloud and DevOps

What is Filebeat and why is it imperative?

Mon, 18 Apr 2022 11:20:32 +0000

The beats are open-source and lightweight data shippers. You can install these as agents on your servers to dispatch operational data to Elasticsearch. You can send data directly via beats to Elasticsearch or via Logstash. Here (in Logstash) you can further process and develop the data.

Filebeat creates a low memory footprint to forward and centralize logs and files and you don’t need to use SSH especially when you have numerous servers, virtual machines, and containers that create logs.

Filebeat is a logging agent. You can install it on the machines that create the log files. Filebeat forwards the data to Logstash or directly into Elasticsearch for indexing.

Filebeat Processors

If you are not using Logstash but still want to process/customize the logs before sending them to ElasticSearch, you can use the Filebeat Processors. You can decode the JSON strings, add various metadata (e.g. Docker, Kubernetes), drop specific fields, and more.

If you want to send logs from Filebeat directly to ElasticSearch this provides benefits such as

We were using logstash for parsing the logs to elasticsearch. But, due to heavy traffic, logs were getting lost and delayed on the elasticsearch. We have modified the workflow and parsed the logs from filebeat to elasticsearch. Now, We are getting the logs in real-time and there is no delay in the logs coming to elasticsearch.

The below Image shows the heavy traffic(6 Million Hits in 15 Minutes) coming from pods and the filebeat is shipping the data in minimal time.

So, The filebeat can handle heavy traffic.

It can support encryption
Decreases the latency involved with the log processing by a middle component like Logstash.

Logstash performance works on a few factors, e.g.

The number & complexity of filters you used
The number of filters, output workers & available system resources.

How do you tune your internal pipeline changes over recent versions? Hence offering your config and versions of Logstash helps you with how you tune it in the best possible way. Besides that, Logstash processing will be limited by the throughput of the slowest output.

3. Cost Reduction

If you ship the logs from filebeat to elasticsearch, you will save the cost of resources consumed by the Logstash pods.

4. Logs will get reflected in ElasticSearch in real-time.

The below Image shows the pod logs timestamp

As shown in the below screenshot, At the same time a log was found on Kibana.

5. There is no loss of logs.

When we were passing the logs through the logstash to Elasticsearch. Logs count on the microservices pod and logs in the Kibana were not matching. We noticed only 20% of the microservices pod logs were there on Kibana. After modifying the workflow, Logs count was matching.

Processors

You can define processors in the Filebeat configuration file per input. To define a processor:

you define the processor name
an optional condition
a set of parameters

Where:

specifies a processor that accomplishes some kind of action, for example choosing the fields that are already exported or adding metadata to the event.
specifies an optional condition. If the condition is present, then the action is executed only if the condition is met. If no condition is set, then the action is always executed.
is the list of parameters to pass to the processor.

Let’s take an example where we have 2 APIs (vehicle and furniture APIs) and each API has multiple microservices.

These microservices are deployed in the Kubernetes cluster as Deployment.

We use Filebeat Autodiscover to fetch logs of pods.

Decode logs are structured as JSON messages using JSON Options.

Add Kubernetes metadata into the log so that we can add fields based on the Pod label.

Pod labels will be present under Kubernetes. labels field, e.g. app label is present under Kubernetes.labels.app.

Use the add_fields processor to add fields such as api_name and microservice_name.
Use the conditional to specify when to add these fields.
To add fields at the top level, set the target to an empty string.
Use the drop_fields processor to remove unwanted fields.

Filebeat output

Finally, send logs to ElasticSearch using the Output.

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Manan Tank is a passionate cloud expert. He is a fast learner and contributes to our major projects. In his free time, he loves to travel and do road trips.

Cloud Champion

Nilesh is a cloud expert. His hardwork is helping us to be at the forefront of CX. In his free time, he loves to sketch and play video games.

Why We need Process Mining?

Fri, 08 Apr 2022 11:52:57 +0000

With the changing economic environment, today’s businesses are embracing process-based approaches to achieve their business goals. Organizations depend on BPM tools and business process modeling to bring operational excellence. In this blog, I am discussing what process mining is and why it is important for applications.

What is Process Mining?

Process mining is a family of techniques relating to the fields of data science and process management to support the analysis of operational processes based on event logs.

What is a process?

Let’s get back to basics by defining processes first.

A process is a series of actions or steps repeated in a progression from a defined or recognized ‘start’ to a defined or recognized ‘finish’.

The purpose of a process is to establish and maintain a commonly understood flow that allows a task to be completed as efficiently and consistently as possible.

Every business process step leaves a digital footprint in your transactional systems in the form of event log data. It is using this data that process mining software works to create a living picture of what your actual processes really look like, which does not always match the definition of that particular process.

Of course, some process steps don’t necessarily take place in a transactional system either sending an email or opening a spreadsheet, for example.

Task mining is the technology that collects that user’s desktop data. Combined with process mining, it gives the most complete view of how work gets done within an organization

What is Process Mining?

Process mining is defined as an analytical discipline for discovering, monitoring and improving processes as they actually are and not as you think they might be.

Process Mining works by extracting knowledge from event logs readily available in today’s information systems, in order to visualize business processes and their every variation as they run.

Need help in process mining?

Why Process Mining Is Important For Any Application?

To explain the answer to this question, I want to talk about one of my past experiences. I was working on one ecom application for one of my clients. We were using a logger table to track the activities of users on our website.

Suddenly, one night during rush hours customers started facing errors like the website was unavailable. I’ve refreshed the table, deleted some of the old records, added indexes and where clauses, and fixed that performance issue. In between, the client lost some of his orders and that was his loss.

Then we researched process mining techniques that will track the errors and exceptions from event logs and provide automated resolutions depending upon the tracked error or exception. due to a huge number of records on a particular table.

I’ve refreshed the table, deleted some of the old records, added indexes and where clauses and fixed that performance issue. In between, the client lost some of his orders and that was his loss.

After that, we scheduled one meeting and tried to track down the exact solution for this kind of issue and it will never happen in the future in any case.

There will be an automated process to resolve that issue at an instance.

Then we have researched process mining techniques that will not only track the errors and exceptions from event logs, but also provide automated resolutions depending upon the tracked error or exception.

What are common business processes?

Common business processes include Purchase-to-Pay (P2P), Order-to-Cash (O2C), or Customer Service processes, for instance.

And while nearly every company has some version of these processes as the backbone of their business, there are many others that support a company’s daily operations, including:

Accounts Payable processes
Accounts Receivable processes
Procurement Processes
Order Management processes
Inventory Management processes

These are just a sample, of course. Moreover, different companies define certain processes differently depending on their business needs, the systems they use, and other variables.

By their nature, processes are not static — nor do they always follow the path defined for them. Even the best-made plans go awry, and over time these deviations can become the rule without continual business process management and process improvement.

Dynamic markets also force change: customer expectations, new product lines, acquisitions, changing geographies, and any number of other things can impact a process’ ability to perform at full capacity.

This is where process mining comes in, as it enables process owners to find and fix inefficiencies in their business processes by giving them complete real-time visibility.

How to Implement Process Mining techniques?

The input for process mining is always event logs or logs which we have in our application. There are a lot of third-party tools as well as services available by which we can implement process mining.

The tool we used at that time was Celonis. There are demos available on the official Celonis website on how we implement the same in our application.

There is a free membership available as well to check if this is definitely helpful for your business or not. We used our event log table as the input for Celonis.

We can use excel/csv or any integration tools output like oracle, sap, or Salesforce. Celonis analyzes data and provides behavior depending upon the files you have uploaded. Sample file attached.For the trial, we can start with a sample file.

This is the sample process mining. After analysis we can track and add action for any specific error and then that will be output to our application. Here we can monitor, track and analyze the performance of the system.

What is the need of Process Mining?

With process mining, organizations can expose what’s really happening in their processes instead of operating under assumptions. They can then identify the root causes of bottlenecks in real-time, optimize their resources and scale with full productivity and confidence.

Ashwini Joshi

Team Lead

Ashwini Joshi is a cloud expert. She is handling major projects with us and a passionate Bharatanatyam dancer. In her free time, she loves to paint too.

Integrated Razorpay within 2 Weeks to Create Flawless Invoicing Experience

Fri, 01 Apr 2022 12:35:59 +0000

Everything from ordering a meal, booking a cab, to pay your bill has shifted to mobile today. And without any confusion, mobile is leading this revolution.

Today’s shoppers are making mobile the biggest disruption in consumerism to browse everything online than the other devices. With ‘mobile first’ becoming the norm, mobile apps are not only on the top chart list but are also impactful.

According to recent research, 69.4% of users use their mobile phones for online shopping.

Let’s have more insights into this mobile-first journey:

6 out of 10 shoppers say that the possibility of shopping via mobile is an essential factor in brand selection.
Mobile web traffic is responsible for 54.8% of international web traffic.
70% of the total digital media time in the US is spent on smartphones.

With ‘mobile first’ becoming the benchmark, mobile apps are growing exponentially. And with this collecting payments from customers is rising.

The COVID-19 pandemic has removed the ‘Cash on Delivery’ option from all online shopping apps due to chances of infection; integrating secure and easy-to-use payment gateways has become the only choice for all online retailers.

Payment is one of the most important steps in the customer journey and hence the online apps should assure the payment solutions or gateways they integrate must deliver an optimized and flawless payment experience. Besides, the payment experience always looks like an extra extension of the whole app experience.

Right from interactive UI to fraud protections, there are many things you should consider before integrating your app with a payment gateway. Here are some:

Ease of Integration: The integration should be easy and take a minimum time for the developer. Also, the update should be easily handled by the merchants, and the SDK size should be lighter so that it won’t take up much space in the user’s device.
User Interface: The UI should ensure the customer a secure gateway and it should land on a mobile-friendly bank page. You should also consider customization as a key aspect while you integrate a payment gateway.
Payment Analytics: Due to several aspects, including double payment, refunds, dropoffs, etc. analytics is important while integrating. Such analytics include average page loading time, the mobile network used (2G, 3G, 4G, etc.), receipt of bank OTP, and more. This helps to identify and resolve issues related to such issues.

Nifty Features and Plugins: Nifty features like auto-reading OTP enhance customer experience by saving a lot of manual efforts. Card-saving feature is another benefit that makes purchasing easy for customers. Razorpay offers both of these features.

Settlement Period: Fast payment settlement is one of the most important features you should consider before integrating a payment gateway. It ensures seamless purchasing and also boosts customer experience.

Why Razorpay and how do we integrate for a customer?

Razorpay is one of the top payment solutions and one of our customers wanted to integrate the same with their app. A slight issue occurred. The built-in API of Razorpay creates invoices that look like a challan. Invoice generation in Razorpay is more of a challan type of bill which has a button for payment to be done. And it does not cater to the need for an Invoice.

Where both challan and invoices are different from each other.

So what we did was, developed a code to create invoices using AWS Lambda and AWS DynamoDB with AWS serverless. It enabled fetching data from tables and payments.

The invoice needs some parameters like SGST and CGST as per the client’s requirements. Also, the client logo needed to be put on the invoice. Using serverless and S3, we could achieve this. The invoice generation is integrated with the payment verification webhook. As soon as a payment is verified and successfully received, an invoice generation lambda function is called. The generated invoice was done using python canvas.

We created a downloadable invoice stored in the S3 bucket for future retrieval.

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Meet Jethwa

Tech Lead

Meet carries extensive work experience and currently leads our organization as an efficient Tech Lead. He loves to learn new technologies. In his free time, he likes painting, sketching, coding and cooking.

Save Your Time With AMI Cleanup Pipeline

Tue, 29 Mar 2022 12:15:42 +0000

What is the AMI Cleanup Pipeline?

The AMI Cleanup pipeline helps you clean up your old unused AMI and related snapshots.

Goals of AMI Cleanup Pipeline

An Amazon Machine Image (AMI) is a packaged environment containing a software configuration and other parts required to set up an instance.
As it contains the built solutions/artifacts, we end up creating AMIs for every new feature or release or even for testing the changes.
Thus, to clean up the older AMIs, we can use this AMI Cleanup Pipeline.

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Benefits of AMI Cleanup Pipeline

The AMIs built into your AWS account have a heavy impact on your monthly bill. So in order to optimize cost, we must delete the unused AMIs since they are increasing the cost even if they are used or not.The AMI cleanup pipeline will help you delete these old unused AMIs.These AMIs are used by snapshots that can not be deleted before de-registering the AMI.

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Key features of the AMI Cleanup Pipeline

AMI

An Amazon Machine Image (AMI) offers the information needed to launch an instance. You must define an AMI when you initiate an instance.

Amazon EBS Snapshots

You can back up the data on your Amazon EBS volumes to Amazon S3 by taking point-in-time snapshots. Snapshots are incremental backups. This implies that only the blocks on the device that have changed after your latest snapshot are saved. It decreases the time needed to create the snapshot and saves a lot on storage costs by not duplicating data. Each snapshot contains all the information that is needed to restore your data (from the moment when the snapshot was taken) to a new EBS volume.

Our Solution Architecture with AMI Cleanup Pipeline

We integrated the AMI Cleanup Pipeline in our Devops-in-a-box platform to deliver better solutions to our customers.

Need AWS Consultation?

Well, we are an Advanced AWS consultant now.

How we help one of our customers with the AMI Cleanup Pipeline?

Our customer’s app involved autoscaling-based deployment. The Autoscaling group helps us to scale out (add EC2 instances) to match an increased load and scale in (remove EC2 instances) to match the decreased load.

So whenever any instance goes down, the autoscaling group handles it quickly by launching another instance with the help of AMI.

Amazon Machine Image (AMI) provides the information required to launch an instance. You must specify an AMI before you launch an instance. Our customer’s app is an EC2-based application.

Let’s say in the app we want to add a new feature, so we will write code for it and create an AMI and we will push the code changes to development/production.

Again, we have a new feature to be added on top of the previous one, so we again repeat the same procedure. So for 10 new features, we created 10 new AMIs.

Also here we are not considering the AMIs that we created for testing purposes, so let’s say we created 5 AMIs for testing each new feature that itself creates 50 AMIs for 10 new features.

But we don’t need the AMIs that we created in the beginning, as they are of no use to us. If we don’t delete these old AMIs we will end up paying for AMIs, that are old and unused.

Instead of manually finding old and unused AMIs and de-registering them, in our client’s app we used AMI Cleanup Pipeline which de-registers all the old, unused AMIs automatically.

When you deregister an AMI, it doesn’t affect any instances that you’ve already initiated from the AMI or any snapshots created during the AMI creation process. You’ll carry on to incur usage costs for these instances and storage costs for the snapshot.

Therefore, you should terminate any instances and delete any snapshots that you’re finished with. The snapshots cannot be deleted before registering AMIs. For cleaning up the old unused AMIs we can use python or bash script.

In python, we have a Boto3 library that can be used to access/connect with AWS resources. We will execute a python program in our pipeline to de-register the AMIs.