AI Engineering: Building Applications with Foundation Models
Product ID: 658892541
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Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an desertcart bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly) . Review: Awesome pick! - Very detailed and well explained! Anyone with basic knowledge of Computer Science can read this book. I just finished the first chapter. This book gives a high level overview of AI application development. Review: Very helpful in breaking down complex AI aspects!! - Love this book. Very insightful and extremely helpful in breaking down many of the complex aspects of AI so they are easy to understand!!!
| Best Sellers Rank | #2,574 in Books ( See Top 100 in Books ) #1 in Machine Theory (Books) #1 in Enterprise Applications #1 in Natural Language Processing (Books) |
| Customer Reviews | 4.7 out of 5 stars 811 Reviews |
A**M
Awesome pick!
Very detailed and well explained! Anyone with basic knowledge of Computer Science can read this book. I just finished the first chapter. This book gives a high level overview of AI application development.
S**A
Very helpful in breaking down complex AI aspects!!
Love this book. Very insightful and extremely helpful in breaking down many of the complex aspects of AI so they are easy to understand!!!
N**A
Best to start with!
Reading this book was fun and helped me connect dots of concepts that hitherto felt like just buzz words. After reading this book, I was prepped enough to read on higher level books. Highly recommended if you're just starting to learn AI and LLMs.
K**S
Best Tech book of 2025!
A bit pricey to what I usually buy, but I can confidently say "You get what you pay for"! I am so jealous of the author's clarity and easy tone that somehow manages to convey an impressive amount of information. In technical writing, if it looks easy, it certainly wasn't! If I survive the technopocalypse, I look forward to reading more of Chip's books!
D**O
Great comprehensive book on the subject
Great comprehensive book on AI engineering. This book simplifies the concepts and techniques of advanced AI development with practical applications across Generative AI
D**N
Excellent Book.
I don't normally have the time for reviews anymore but I had to do one for this book. This book is excellent. The level of detail and range of topics was just right. With some books I've had to force myself to finish. This one kept me interested throughout the entire book and provided everything clearly. I'm more interested in usage of foundation models (LLM's, RAG, etc.) but the chapters on model pre-training/training/evaluation provided great detail. I'm looking forward to more works from Chip.
S**U
A must read for everyone in IT!!
Can't describe in words how important this book is in today's tech landscape. It covers all required ground required to learn GenAI concepts in detail with ease.
T**G
Great comprehensive survey of AI system
Comprehensive survey of AI field today. Good introduction to different aspects of AI, instead of focusing only on the models.
ع**ي
نسخه جيده وطباعه واضحه سهل الفهم وثري بلمعلومات
كتاب رائع تغليف جيد والورق والكتابه واضحه سرعه بشحن وتوصيل المعلومات فيه قيمه جدا جدا دخفت دورات كثير ماأستفدت زي هذا الكتاب أنصح فيه وبشده مممتع جدا وسهل الفهم
I**A
amazing book
I still need to learn more technical things to be able to understand all knowledge that this books brings but I learned a lot and will use as guide on this process. I strong recommend to anyone that want to start and don’t know where start
J**L
Great overview
The central idea of the book is that foundation models have become so powerful and expensive to build that, instead of training models, many organizations might be better off creating applications on top of them. The book covers evaluation, guardrails, security, finetuning, context construction, inference optimization, user feedback and architecture. The level of detail is excellent: we're looking under the hood just enough to understand what's going on, but keep that high level perspective that allows the book to give a overview of a broad topic in just 500 pages. I highly recommended this book to engineers looking for an overview of AI engineering — as opposed to ML engineering, which might be too low-level for them and be more relevant for data scientists.
A**S
Amazing book
If you are working with LLMs, this book is a great read. I really liked how it treats foundation models as a new software stack rather than just “better models”, covering the full lifecycle from model selection and adaptation (prompts, RAG, fine‑tuning, agents) to evaluation‑driven development and deployment trade‑offs around latency and cost. P.S. this book has the most interesting footnotes!
S**H
Book and delivery are good
Fantastic book and great and timely delivery
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