Quality Assurance in AI-Based Systems – Ensuring AI Projects Do Not Fail. Numerous examples from various industries. Many practical exercises with sample solutions. With excursions based on industrial project experiences. Surveys in the industry clearly show: AI projects fail more often than assumed. Therefore, continuous quality assurance for AI-based systems is essential. The author team provides a solid overview and a good introduction to the concepts, best practices, challenges, and solutions related to quality assurance of and with AI-based systems. The focus is on testing AI-based systems. Specifically, the following topics are covered: Quality criteria for AI-based systems, testing AI-specific quality characteristics, testing methods and techniques for AI-based systems, testing environments for AI-based systems, and testing with AI. The book is aligned with the ISTQB Syllabus Certified Tester AI and is not only excellent for exam preparation for the "Certified Tester AI Test" but also serves as a compact foundational work on the topic in practice and at universities.