AI Engineering: Building Applications with Foundation Models
Original price was: $79.99.$52.40Current price is: $52.40.
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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 Amazon bestseller in AI.
AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O’Reilly).
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Publisher : O’Reilly Media
Publication date : January 7, 2025
Edition : 1st
Language : English
Print length : 532 pages
ISBN-10 : 1098166302
ISBN-13 : 978-1098166304
Item Weight : 2.05 pounds
Dimensions : 6.9 x 1.1 x 9 inches
Best Sellers Rank: #5,371 in Books (See Top 100 in Books) #1 in Enterprise Applications #1 in Machine Theory (Books) #1 in Natural Language Processing (Books)
Customer Reviews: 4.6 4.6 out of 5 stars (503) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });
13 reviews for AI Engineering: Building Applications with Foundation Models
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Original price was: $79.99.$52.40Current price is: $52.40.
John Lockney –
A superb technical guide.
This book is so incredibly well-organized and well-edited that it’s hard to believe it’s a first edition rather than a third or fourth. I love when authors invite readers to jump around and read chapters out of order; in this case, every section proved helpful. This book offers a clear and valuable overview of AI, I plan to keep it close at hand as a reference.
David E. Hallman –
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.
Scott J. Pearson –
The best intro to AI engineering I’ve encountered
It’s always daunting to pick up a technical book that’s over 500 pages long or 21 hours long. However, this book did not disappoint. Not every section, of course, addressed my particular needs. However, the entire treatise was clearly communicated with a broader technical audience in mind. That should be no surprise because Chip Huyen, besides being an AI expert, taught graduate school classes in AI at Stanford and writes science fiction as a side hobby. This book is simply the best technical introduction I’ve encountered to date.The book starts with high-level concepts about AI, which would be accessible to all sorts of scientific folks. Then it focuses on technical topics that are of most interest to engineers. It does an excellent job of centering around concepts first and not being wedded to particular technologies which will soon change. I valued the insights so much that, after listening to the audiobook, I even bought a paper copy to have for a reference.I plan to continue to read about AI engineering, but given that I haven’t taken formal coursework in the topic, this book served as an equivalent to a graduate school class to give me confidence to dive deeper. Although some math were presented, the audiobook was incredibly accessible, unlike with some technical books. For those who spend time commuting in cars, I recommend listening to the text if you don’t have time to flip through a paper book.Overall, this book raised my game significantly about AI. Where other books obscure with technical jargon, this book enlightens with clear concepts. I still need to brush up on a few focused topics to ready myself for a project, but I’m much more fluent about the ideas than before. I highly recommend this in-depth introduction, at least for the next few years until the field outpaces our knowledge once again.
Mahmoud Youssef –
The best book on AI Engineering
The best book on AI Engineering. I enjoyed reading every page of this book. The book cover the whole breadth of the AI engineering field from the platform to the application itself covering very important topics including evaluation and security among others. The book covers all the different approaches for addressing each issues in the application design including the approaches from research and academia. The author has an amazing writing style that is fun to read and learn from.
Denise Shekerjian –
Your new best friend in AI engineering.
Chip Huyen has done it again—delivering a smart, thorough guide that takes readers step by step through complex material with remarkable clarity. Through simple, accessible examples, she empowers readers to achieve their goals. The modular structure allows experienced readers to navigate at their own pace, while her unmatched coverage of practical applications sets this work apart.Her approachable tone builds reader confidence, ensuring full comprehension of the material. Well-documented and diverse sources provide a robust foundation, while her presentation style—concise, clear, and thoughtfully structured with short, easy to digest paragraphs—creates an ideal learning experience. Important points and deeper insights are segregated and clearly marked for easy reference.This resource will undoubtedly become a valued reference, likely to evolve alongside the field itself. Thank you, Chip! A worthy successor to your first volume — and we eagerly await your next contribution to the field. ~ Denise Shekerjian, author Uncommon Genius (Viking, Penguin)
Darryl Romano –
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
Alexis Chilton –
Great breadth and depth of topics; covers theory, prod, and biz.
Fantastic book, very useful skills to apply to my job as an MLE immediately! Each chapter goes super in-depth and covers different approaches, weighing the pros and cons of each. Chapters discuss theory, production, and business, with a ton of citations and visuals throughout.
K B –
Foundational Knowledge – Should be Required Reading
Chip Huyen covers AI engineering with an approach that is both exhaustively well-sourced and somehow still approachable. No matter where you are on the spectrum of this field, from expert to novice, pick this up and find yourself (and your output) better for having done so.
Julien Zaegel –
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.
Kindle Customer –
The book had exactly the level of depth I needed. I’m coming from the data engineering side and needed some complete overview of AI Engineering. The book gave a complete coverage of the key topics while still going with some details (but avoiding the non-necessary technicalities). The reference are really valuable and worth the de-tour while reading.
G. D. Gobiratnam –
Chip Huyen’s AI Engineering: Building Applications with Foundation Models offers a concise yet comprehensive exploration of the core concepts that underpin modern AI engineering. In an era where AI tools, frameworks, and APIs evolve almost weekly, designing a coherent, durable book is no small feat—and Huyen succeeds admirably.The book is firmly grounded in theory, supported by clear diagrams that help illuminate complex ideas. While it doesn’t delve into code snippets or implementation-heavy examples, this feels like a deliberate choice rather than a shortcoming. The restraint in length is actually a strength: it makes the book more digestible, especially for readers who want to understand foundational principles without getting bogged down in fast-aging technical details.One of the biggest challenges in writing about AI today is the pace of change. Huyen avoids the trap of chasing trends and instead focuses on building conceptual clarity—something far more enduring. Whether you’re a software engineer looking to transition into AI, a data scientist aiming to deepen your understanding of systems, or a product leader wanting to make more informed decisions, this book provides the scaffolding you’ll need.I couldn’t recommend it more highly for anyone looking to master AI engineering or familiarize themselves with its essential concepts. This is a book you’ll want on your shelf—thoughtful, structured, and refreshingly free of unnecessary fluff.
john ireland –
All words and no substance – not even a single diagram or flowchart to illustrate a process – to call it ‘engineering’ completely misses the point.
Ivan Diaz –
Best book on AI engineering. It is not based on technologies, but on principles and patterns. It is a MUST if you start with agents development or if you just want to know about AI topics.