Quality Automation & Testing | Applied AI Consulting

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.

Quality Automation & Testing | Applied AI Consulting

ML Testing: Ensuring Accuracy and Reliability in Machine Learning Systems

Level 1: Smoke Testing

Level 2: Integration Testing and Unit Testing

🔢 Data

🤖 Models

Post-train tests

Production

ML model testing : performance metrics and evaluation method

Related posts

API Automation: Best Practices for Testing and Development

Phase 1: Getting everything ready

Setting up Postman

Listing/New Request

Phase 2: Automated Tests Development

Random Data Generation

Mocking APIs

Phase 3: Automated Tests Execution

Verify ‘User is Deleted’ Request

Conclusion

Related posts

Driving Quality and Reliability for modern mobile and web applications

Challenge & Test Requirements

Solution and Approach

ROI

Results

Related posts

Accelerating QA and Ensuring Performance: A Comprehensive Test Automation Case Study

Challenge & Test Requirements

Solution and Approach

ROI

Results

Related posts

Enhancing Healthcare Application Quality with Applied AI Consulting’s QA Automation Service

Challenge & Test Requirements

Solution and Approach

ROI

Results

Related posts