AI Engineering — Building Applications with Foundation Models.
Recent breakthroughs in AI have lowered the barrier to entry so much that the model-as-a-service approach has turned AI from an esoteric discipline into a development tool anyone can use — including people with minimal or no prior AI experience. That is the premise Chip Huyen builds this book around, and it is exactly the shift agency owners in our space are trying to capitalize on.https://amzn.to/44URCsB
Huyen, previously with Snorkel AI and NVIDIA, founder of an AI infrastructure startup, and former Stanford Machine Learning Systems Design instructor, starts with how AI engineering differs from traditional ML engineering and the new AI stack this shift has created. From there, the book covers model adaptation techniques including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, explaining not just how but why each works. It also tackles the practical bottlenecks of latency and cost when serving foundation models, and how to choose the right model, dataset, and evaluation metrics plus a substantial section on evaluation itself, since more AI usage means more opportunities for catastrophic failures.https://amzn.to/44URCsB
This is a step up in technical depth from most “beginner AI” titles, it builds on Huyen’s bestselling “Designing Machine Learning Systems” and assumes you are serious about production-grade work, not just prompting.
Who this is for: technical founders, agencies with in-house engineering talent, or consultants advising clients on model selection, evaluation, and cost trade offs, this is a reference book you will return to, not a one-time read.https://amzn.to/44URCsB
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