Designing Machine Learning Systems provides a practical, end-to-end guide for building scalable, reliable, and maintainable ML applications. Chip Huyen presents an iterative approach to designing ML systems, covering key aspects such as data engineering, model deployment, monitoring, and continuous improvement.

This book moves beyond theoretical ML models and focuses on real-world implementation challenges. It explores how to build production-ready ML pipelines, tackle model drift and bias, and design resilient, scalable architectures. Huyen also discusses MLOps best practices, highlighting the need for collaboration between data scientists, engineers, and business teams.

Why Read This Book

• Gain a structured, iterative approach to designing machine learning systems.
• Learn practical techniques for handling data, deploying models, and monitoring performance.
• Understand challenges like model drift, feedback loops, and scalability.
• Explore MLOps practices to streamline ML workflows in production.
• Written by Chip Huyen, an expert in ML systems, startups, and industry applications.

About the Author

Chip Huyen is a machine learning engineer, entrepreneur, and educator. She has worked with leading AI companies and startups, focusing on ML infrastructure and production systems. Previously, she taught machine learning systems design at Stanford University and co-founded Claypot AI, a real-time machine learning platform. Huyen is also a writer and speaker, known for simplifying complex ML concepts into actionable insights.