Testing Machine Learning and GenAI Systems

About this course

This course is designed for QA Engineers, Automation Engineers, and SDETs who want to learn how to test Machine Learning (ML) and Generative AI (GenAI) systems across their complete lifecycle.

Traditional software testing techniques are not sufficient for AI/ML systems, where behavior depends on data, probabilities, and model decisions. This course teaches practical, real-world testing strategies to validate accuracy, reliability, fairness, robustness, and performance of ML and GenAI models.

You will learn how to test AI/ML systems at every stage:

• Early-stage testing during model development
• Functional and evaluation-phase testingAPI-level automation for ML models
• Responsible AI testing for bias, fairness, and ethics
• Post-deployment monitoring and drift detection

The course includes hands-on demos, real-world examples, and quizzes, covering supervised, unsupervised, reinforcement learning models, and Retrieval-Augmented Generation (RAG) systems.

By the end of this course, you will be able to design and execute comprehensive testing strategies for AI/ML systems used in enterprise environments.

What you will be able to do after this course

Understand ML and GenAI systems from a QA testing perspective

Perform early-stage testing during model development

Validate ML model accuracy, consistency, and behavior

Design API automation tests for ML model endpoints

Test prompt behavior and response stability in GenAI systems

Apply responsible AI testing for bias, fairness, and transparency

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