Testing AI & Machine Learning Systems: Ensuring Quality, Fairness & Reliability gives you the essential tools to ensure your AI models are trustworthy, fair, and dependable.

Traditional QA doesn’t work for AI. This book shows you how to test what matters most: data quality, bias detection, model performance, robustness, and production reliability. With clear explanations and hands-on exercises, you’ll learn to validate machine learning pipelines, monitor models in production, and apply advanced testing strategies across industries like finance, healthcare, and e-commerce.

Inside you’ll discover:

Proven methods for AI bias testing and fairness validation.

Frameworks for data quality assurance and drift detection.

Tools for robustness, stress, and adversarial testing.

End-to-end testing practices for MLOps pipelines.

Real-world case studies from Fortune 500 companies.

With 75+ exercises, 25+ case studies, templates, and tool comparisons, this book bridges QA and data science—giving you practical skills to build AI systems that perform reliably and responsibly.

Perfect for QA engineers, test leads, MLOps practitioners, and data scientists, it’s your complete guide to mastering AI quality assurance.