Includes bibliographical references and index.

Overview of machine learning systems -- Introduction to machine learning systems design -- Data engineering fundamentals -- Training data -- Feature Engineering -- Model development and offline evaluation -- Model develoypment and prediction service -- Data distribution shifts and monitoring -- Continual learning and test in production -- Infrastructure and tooling for MLOps -- The human side of machine learning

"Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references."--Amazon.com.