Designing Machine Learning Systems

Designing Machine Learning Systems by Chip Huyen offers the comprehensive, end-to-end guide that ML engineers and data scientists have long needed to bridge the gap between academic model-building and the messy realities of production. Rather than treating the ML algorithm as the centerpiece, Huyen situates it as just one small component within a much larger system encompassing business objectives, data pipelines, feature engineering, deployment infrastructure, monitoring, and responsible AI. Drawing on her experience at NVIDIA, Netflix, Snorkel AI, and Stanford—where she teaches the course CS 329S: Machine Learning Systems Design—she walks readers through every stage of the ML project lifecycle with concrete case studies, trade-off discussions, and practical frameworks. The book covers everything from sampling strategies and labeling techniques, through model development and offline evaluation, to online prediction, data distribution shift detection, continual learning, MLOps infrastructure, and the human and ethical dimensions of deploying AI at scale. Whether you are deploying your first model or managing hundreds in production, this book provides the principled vocabulary and decision-making framework to do it right.

What it argues

A causal model describing how design levers across the ML lifecycle—data quality, feature engineering, model development practices, deployment architecture, monitoring infrastructure, and organizational structure—produce intermediate system and behavioral states that ultimately drive production ML outcomes including reliability, business value, and responsible deployment.