AI Engineering: Building Applications with Foundation Models
Product ID: 689419709
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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: Good AI book to read.. - The book was delivered in good condition. The contents of book are good. Although Agentic AI topics like LangChain, LangGraph etc. are not covered here. Also topics like MCP are not covered here. But overall it's a good book to read. I am yet to read this book. Review: AI for app engineers - Surprised with the colour diagrams! Very good quality book. Coming to content of the book, it's a must read for someone to get idea on how to adapt AI in the applications and get good insight about AI terminologies. Very well structured and simple english makes it reading intresting.
| Best Sellers Rank | #110,533 in Books ( See Top 100 in Books ) #2 in Computer Science Books #4,136 in Analysis & Strategy |
| Customer Reviews | 4.4 out of 5 stars 914 Reviews |
H**I
Good AI book to read..
The book was delivered in good condition. The contents of book are good. Although Agentic AI topics like LangChain, LangGraph etc. are not covered here. Also topics like MCP are not covered here. But overall it's a good book to read. I am yet to read this book.
A**H
AI for app engineers
Surprised with the colour diagrams! Very good quality book. Coming to content of the book, it's a must read for someone to get idea on how to adapt AI in the applications and get good insight about AI terminologies. Very well structured and simple english makes it reading intresting.
J**A
Good Read
Very detailed and clearly articulated on the topics covered in this book. Good for both technical and non-technical users to read.
R**A
But missing pages. But reading is worth the time and money.
Enjoying the read. Might be a little boaring at times but it's worth the time and price. The first copy that I received had missing pages so please check for all the pages as soon as you receive it.
A**S
Must read for ai professional
Great learning 👍 lot of information to gain knowledge pease keep writing more such books like this one. Thanks 😊
P**H
Great content
Not a how-to practical book, but still a valuable resource in learning AI system. Great content and excellent writing style.
A**R
Awesome
Knowledgeable book and nice explained
D**K
Amazing book 😍
Love it. This book is especially for you if you have no background in ML engineer and more specifically AI engineering. It will give you every bit of information using which you can make informed choices in your career and work. Highly recommended!
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
A**R
Do yourself a favor and read this fantastic book!
Chip is master at explaining complex concepts in an easy to understand words, with lots of practical examples! I was not expecting less after reading her first book tho!
K**R
Broad overview of AI Engineering
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.
A**S
An Essential Roadmap for Navigating the AI Disruption
If you feel like the ground is shifting beneath your feet as an engineer, you aren't imagining it and Chip Huyen’s AI Engineering is one of the best guides to make sense of the chaos. Our February 2026 Data & AI Book Club recently dissected this book, and the consensus was clear; it’s a manual for the new reality of software development. Whether you are a seasoned architect or a developer just starting to integrate agentic workflows, this book provides the framework to stay relevant. It’s not just about learning tools; it’s about understanding the shift in economics and strategy that defines the current year. AI is a fast evolving space and so much has already happened since the book was published. We look forward to a second edition that includes AI engineering with vibe coding, AI platforms and multi agent systems.
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