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Keep shoppingTesting Machine Learning and GenAI Systems
60 students
Testing Machine Learning and GenAI Systems
Functional, API, and responsible testing of ML and GenAI systems (2026)
Created byPrateek Sethi
Last updated 1/2026
English
What you'll learn
- Upon completing this course, QA , SDET, QA Test Automation professionals will be equipped to:
- Effectively Test and Validate ML Models.
- Implement ML-Specific API Testing and Automation.
- Monitor and Manage Models for Ongoing Quality Assurance
- Ensure Responsible and Ethical AI through Rigorous Testing
- Selecting the Best Model for a Business Problem.
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Course content
12 sections • 33 lectures • 3h 4m total length
Requirements
- This course is designed to be accessible to both beginners and experienced QA professionals looking to expand their expertise into AI and ML testing. To get the most out of this course, here are a few helpful (but not mandatory) prerequisites:
- Basic Understanding of Software Testing Principles.
- Interest in Machine Learning Concepts. No prior experience with ML is necessary, but a curiosity about how machine learning models work will enhance your learning experience.
- Familiarity with Testing Tools (Preferred but Not Required)
- A Laptop or Computer for Hands-On Practice.
Description
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 testing
API-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
Monitor models post-deployment for latency and data drift
Support model selection decisions using testing insights
Test RAG pipelines and document-based AI systems
Who this course is for:
- This course is tailored for QA professionals, SDETs, Data Analysts, and anyone involved in quality assurance who wants to expand their skills into the exciting field of AI and Machine Learning (ML) testing. The course content is designed to help you bridge the gap between traditional software testing and the specialized needs of ML model validation, making it valuable for:
- Quality Assurance (QA) Engineers looking to enhance their testing toolkit with skills specific to AI/ML model reliability, functionality, and fairness.
- Software Development Engineers in Test (SDETs) aiming to stay ahead of the curve by learning how to automate and monitor ML model testing processes.
- Functional and Automation Testers interested in developing new testing strategies for ML models and ensuring their robust performance across different environments.
- Data Analysts and ML Enthusiasts who want to learn the testing practices that can ensure model accuracy and compliance in production settings.
- QA Engineers and Test Automation Engineers
- Engineers transitioning into AI/ML testing roles