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

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen is a practical guide to designing, deploying, and maintaining reliable machine learning applications in real-world environments. Published by O’Reilly Media, this book goes beyond machine learning algorithms to explore the engineering decisions required to build scalable, maintainable, and production-ready systems.

The book covers the complete machine learning lifecycle, including project planning, data engineering, feature development, model selection, evaluation, deployment, monitoring, and retraining. It explains how to make design decisions that balance performance, reliability, scalability, latency, and changing business requirements.

Through practical examples and industry case studies, Chip Huyen demonstrates how teams can build automated workflows for developing, evaluating, deploying, and updating models. Readers also explore infrastructure choices, data quality, model monitoring, and responsible machine learning practices.

Ideal for machine learning engineers, data scientists, software developers, and AI professionals, this book provides a valuable framework for bridging the gap between experimental models and dependable production systems.

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