Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

About the Book – Designing Machine Learning Systems

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen is a practical guide to building, deploying, and maintaining machine learning systems in real-world environments.

The book explores the complete machine learning lifecycle, from collecting and processing data to model development, evaluation, deployment, and continuous monitoring. It explains how to design scalable, reliable, and adaptable systems that meet real business requirements.

Through practical examples, industry case studies, and proven engineering approaches, readers gain insights into data management, feature engineering, model selection, infrastructure design, and responsible AI practices.

Ideal for machine learning engineers, data scientists, software developers, AI professionals, and students, this book provides valuable knowledge for turning machine learning models into effective production-ready applications.

About the Author – Chip Huyen

Chip Huyen is a Vietnamese writer, computer scientist, and entrepreneur specialising in machine learning systems and artificial intelligence. She is known for her work in developing practical AI infrastructure and helping organisations build and deploy machine learning applications.

She has worked with leading technology companies, including NVIDIA, Netflix, and Snorkel AI, and is the co-founder of Claypot AI, a company focused on real-time machine learning infrastructure.

A Stanford University graduate, Chip has also taught machine learning systems design at Stanford. Her teaching experience and industry expertise form the foundation of her bestselling book Designing Machine Learning Systems.

Through her writing, research, and professional work, she aims to make complex AI and machine learning concepts more accessible and practical for developers, engineers, and data science professionals.