Designing Machine Learning Systems

Designing Machine Learning Systems fills a critical gap in the machine learning literature by focusing on the challenges of deploying and operating ML systems in production environments. While many resources teach algorithms and model building, this book addresses the entire ML system lifecycle.

Chip Huyen draws on her extensive experience to provide practical advice on data collection, feature engineering, model selection, testing, deployment, monitoring, and maintenance. The book is particularly valuable for its emphasis on reliability, scalability, and maintainability—concerns that are often overlooked in academic treatments of machine learning.

Throughout the book, Huyen balances technical depth with accessibility, making complex concepts understandable without oversimplifying. Real-world examples and case studies illustrate key points and help readers apply the concepts to their own projects.