AI Testing library

Introduction to AI Testing offers a comprehensive and structured foundation in AI system validation-covering everything from ML fundamentals and neural networks to AI-specific quality metrics, testing techniques, and test environments. An essential read for AI practitioners and testers. Highly recommend it!

This book strikes a pragmatic balance between technical accuracy, topic coverage, and accessibility. The authors skilfully introduce readers to both contemporary AI and its intricate relationship with testing-exploring not only how AI systems can be tested but also how AI can enhance testing practices. Given the complexity of the subject matter, the authors deserve commendation for delivering such a clear and structured introduction.

A clear, practical guide that demystifies AI testing and supports ISTQB certification preparation. Introduction to AI Testing is a valuable companion for professionals building confidence and competence in the field.

Far more than an introduction, Introduction to AI Testing uses its unique 'theory to practice' sections to make complex AI and testing concepts readily understandable. An invaluable resource that lowers the learning curve for any professional.

From the ISTQB(R) CT-AI exam training perspective, this is one of the most usable resources I've come across. It's well-organised, clear, and filled with pictures, graphs and charts that support deeper comprehension. I wouldn't hesitate

In the rapidly evolving era of artificial intelligence, this book stands out as an essential resource for professionals seeking to understand and apply AI testing principles within a structured, industry-recognized framework. It goes beyond the scope of testing alone, offering clear explanations of core Machine Learning and AI concepts and demonstrating how these systems can be effectively tested. A valuable guide for navigating the unique challenges of testing intelligent systems.

It's clear this book was written by professionals with real-world experience testing complex systems in high-stakes industries. The thoughtful focus on structure, performance, compliance, and resilience reflects the kind of rigor required in heavy regulated domain like for example financial services, where reliability and regulatory alignment are non-negotiable. For anyone working with AI systems, this book offers both practical tools and valuable perspective.

The AI and testing concepts are explained beyond their dictionary meaning. Useful questions and hands-on examples review the topics in each chapter. That's ideal for beginners and will give practitioners a path for their transition to AI. The real world examples help improve understanding for project professionals and less technical managers.

This book, with the ISTQB(R) CT-AI syllabus as a backbone, does more than simply prepare readers for certification. It enhances and enriches its content with numerous theoretical and practical examples, rigorously contextualizing the principles while making them accessible to IT quality professionals, and anyone interested in analyzing and understanding this new social phenomenon. It is a solid reference and a valuable resource, not only for passing the certification, but also far beyond, for testing AI or testing with AI. Without a doubt, an essential bedside book for the new generation of QA-ers, and for the older ones that want to keep abreast with progress.

With its methodical approach to AI testing, this book fills a critical gap for engineering leaders navigating AI risk. It equips quality teams to embed accountability, interoperability and trust into intelligent systems.

With the rapid emergence, development and implementation of AI systems, Introduction to AI Testing couldn't be more timely! This book clearly and engagingly describes the context and detail of both AI and machine learning, before moving to describe the required practices to test such systems and govern quality assurance. Whether the reader is new to AI, testing or both - this serves as an excellent resource.