Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications Book by Chip Huyen

Designing Machine Learning Systems by Chip Huyen is a comprehensive guide to building ML systems that work in real-world production environments. This book moves beyond theory to focus on the practical, iterative process of developing, deploying, and maintaining machine learning applications at scale. Huyen covers the complete ML lifecycle—from problem definition and data collection to model training, evaluation, and monitoring. You'll learn how to navigate common challenges like data quality issues, feature engineering, model selection, and handling technical debt. The book emphasizes the importance of iteration and feedback loops in creating systems that deliver business value. Perfect for engineers, data scientists, and technical leaders who want to bridge the gap between academic ML knowledge and production-ready systems. Whether you're building your first ML application or scaling existing systems, this book provides actionable insights and best practices grounded in real-world experience.