AI Engineering
by Chip Huyen
Chip Huyen's practical guide to building production AI systems beyond research prototypes.
"Building AI systems is 10% modeling and 90% everything else no one tells you about.".
Editorial Summary
AI Engineering by Chip Huyen provides a comprehensive framework for designing, building, and deploying machine learning systems in production environments. Rather than focusing on deep learning theory or large language models in isolation, Huyen addresses the full lifecycle of AI engineering—from data collection and model training to serving, monitoring, and continuous improvement. The book draws on real-world case studies from industry leaders and emphasizes practical patterns that distinguish successful AI deployments from failed research projects. Chip Huyen, an AI infrastructure expert known for her work on machine learning systems, offers actionable guidance on tooling, architecture decisions, and organizational practices that enable teams to move beyond experimentation into reliable, scalable AI products.
Perspective
"Reading AI Engineering feels like finally getting a map of the terrain that separates a working demo from a production system — Huyen makes visible all the decisions that most ML tutorials skip, from data pipelines to monitoring to handling model degradation over time. The book's distinctive angle is its relentless focus on what actually fails in real deployments rather than what looks elegant in research papers. Engineers who have successfully trained models but struggled to keep them performing reliably in production will find this the most practically useful book in the field."
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