Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security, and operations. In this course you'll be introduced to quality engineering (QE) for AI and machine learning. You’ll learn the fundamentals of AI and ML, including how intelligent agents are modeled, trained and developed. You'll then dive into approaches for validating ML models offline, prior to release, and online, continuously, post-deployment. By developing and executing a test plan for a live ML-based recommendation system, you'll experience the practical issues around testing AI first-hand.