Designing Machine Learning Systems

Most machine learning education stops at model training, but production ML systems fail far more often at the surrounding infrastructure than at the modeling itself. Designing Machine Learning Systems is built around that reality. Chip Huyen walks through the full lifecycle of a production machine learning system — from framing the problem and building reliable data pipelines through model deployment, monitoring, and the iterative updates that keep a system performing well over time. Each stage is treated as a genuine engineering discipline with real tradeoffs, failure modes, and design decisions, rather than a checklist to move through quickly on the way to a working model. Huyen draws on substantial industry experience building ML systems at scale, and that background shows in how concretely the book engages with real production concerns — data drift, feature stores, serving latency, model versioning, and the organizational dynamics that shape how ML systems get built and maintained in practice. At 386 pages, the book covers substantial ground without becoming padded, staying tightly focused on the production context that makes it distinctive from more training-focused ML resources. For engineers and practitioners who want to build ML systems that actually hold up outside a controlled environment, this has quickly become one of the field’s most trusted references.

Author:
Chip Huyen