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  • AI Engineering: Building Applications with Foundation Models

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AI Engineering: Building Applications with Foundation Models

by Chip Huyen (Author) Format: Paperback
4.6 out of 5 stars (1,018)
#1 Best Seller in Enterprise Applications
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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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  1. ISBN-10
    1098166302
  2. ISBN-13
    978-1098166304
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    January 7, 2025
  6. Language
    English
  7. Dimensions
    6.9 x 1.1 x 9 inches
  8. Print length
    532 pages
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From the Publisher

AI Engineering: Building Applications with Foundation Models

Who This Book Is For

This book is for anyone who wants to leverage foundation models to solve real-world problems. This is a technical book, so the language of this book is geared toward technical roles, including AI engineers, ML engineers, data scientists, engineering managers, and technical product managers. This book is for you if you can relate to one of the following scenarios:

  • You’re building or optimizing an AI application, whether you’re starting from scratch or looking to move beyond the demo phase into a production-ready stage. You may also be facing issues like hallucinations, security, latency, or costs, and need targeted solutions.
  • You want to streamline your team’s AI development process, making it more systematic, faster, and reliable.
  • You want to understand how your organization can leverage foundation models to improve the business’s bottom line and how to build a team to do so.

You can also benefit from the book if you belong to one of the following groups:

  • Tool developers who want to identify underserved areas in AI engineering to position your products in the ecosystem.
  • Researchers who want to better understand AI use cases.
  • Job candidates seeking clarity on the skills needed to pursue a career as an AI engineer.
  • Anyone wanting to better understand AI’s capabilities and limitations, and how it might affect different roles.

I love getting to the bottom of things, so some sections dive a bit deeper into the technical side. While many early readers like the detail, it might not be for everyone. I’ll give you a heads-up before things get too technical. Feel free to skip ahead if it feels a little too in the weeds!

AI Engineering
Ingeniería de IA
Ingegneria dell'IA
Ingénierie de l'IA
Ingénierie de l'IA
Languages English Spanish Italian French German

Editorial Reviews

Review

"This book offers a comprehensive, well-structured guide to the essential aspects of building generative AI systems. A must-read for any professional looking to scale AI across the enterprise."

- Vittorio Cretella, former global CIO at P&G and Mars

"Chip Huyen gets generative AI. She is a remarkable teacher and writer whose work has been instrumental in helping teams bring AI into production. Drawing on her deep expertise, AI Engineering is a comprehensive and holistic guide to building generative AI applications in production."

- Luke Metz, co-creator of ChatGPT

"Every AI engineer building real-world applications should read this book. It's a vital guide to end-to-end AI system design, from model development and evaluation to large-scale deployment and operation."

- Andrei Lopatenko, Director Search and AI, Neuron7

"This book serves as an essential guide for building AI products that can scale. Unlike other books that focus on tools or current trends that are constantly changing, Chip delivers timeless foundational knowledge. Whether you're a product manager or an engineer, this book effectively bridges the collaboration gap between cross-functional teams, making it a must-read for anyone involved in AI development."

- Aileen Bui, AI Product Operations Manager, Google

"This is the definitive segue into AI Engineering from one of the greats of ML Engineering! Chip has seen through successful projects and careers at every stage of a company and for the first time ever condensed her expertise for new AI Engineers entering the field."

- swyx, Curator, AI Engineer

About the Author

Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Through her work at NVIDIA, Netflix, and Snorkel AI, she has helped some of the world's largest organizations develop and deploy machine learning systems. She teaches CS 329S: Machine Learning Systems Design at Stanford, whose lecture notes this book is based on. LinkedIn included her among Top Voices in Software Development (2019) and Top Voices in Data Science & AI (2020). She is also the author of four bestselling Vietnamese books, including the series Xach ba lo len va Di (Pack Your Bag and Go). She also runs a Discord server on MLOps with over 6,000 members (https://discord.com/invite/Mw77HPrgjF).

Product details

  • 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: #2,567 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.6 out of 5 stars (1,018)

About the author

Follow authors for new release and deal updates, plus improved recommendations. See updates from all followed authors in Your Books.
Chip Huyen

Chip Huyen

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

I’m Chip Huyen, a writer and computer scientist. I grew up chasing grasshoppers in a small rice-farming village in Vietnam.

I work in the intersection of AI, data, and storytelling. Previously, I built machine learning tools at NVIDIA, Snorkel AI, Netflix, and founded an AI infrastructure startup (acquired).

I also taught Machine Learning Systems Design at Stanford.

My last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages (very proud!).

In my free time, I like writing stories. I'm also the author of 4 Vietnamese story books.

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Customer reviews

4.6 out of 5 stars
1,018 global ratings
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Customers say

Customers find the book detailed enough to understand the fundamentals and appreciate its comprehensive content for both beginners and advanced users. Moreover, the book comprehensively introduces basic AI knowledge, and one customer notes it goes deeper into trends and algorithms. Additionally, customers find the book practical, with one highlighting its coverage of practical applications across Generative AI. However, the writing quality and readability receive mixed feedback, with some finding it well-written and easy to read, while others describe it as boring and difficult to follow.
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44 customers mention content, 33 positive, 11 negative
Customers praise the book's content, finding it detailed enough to understand the fundamentals while being comprehensive and suitable for both beginners and advanced users.
Great book for beginnersRead more
Excellent content and quality print.Read more
It's dry, unnecessarily lengthy and not informative at all, could be condensed to 10% of it....Read more
A great resource for anyone looking to enter the field of AI engineering....Read more
43 customers mention informative, 37 positive, 6 negative
Customers find the book informative, comprehensively introducing basic AI knowledge with great breadth and depth of topics.
Yes- this is a very informative book.Read more
Love this book. Very insightful and extremely helpful in breaking down many of the complex aspects of AI so they are easy to understand!!!Read more
A dense read, but insightful. Nice work.Read more
Easy to read. Well organized. Chock full of good information. Highly recommended....Read more
13 customers mention practicality, 11 positive, 2 negative
Customers find the book practical and helpful, with one customer noting it serves as an equivalent to a graduate school class.
A well-written and practical book for anyone building applications with foundation models....Read more
This is an easy-to-read, how it's made book, relevant -- just need to look up more recent advancements in some of the author's citing....Read more
...Through simple, accessible examples, she empowers readers to achieve their goals....Read more
Fantastic book, very useful skills to apply to my job as an MLE immediately!...Read more
13 customers mention writing quality, 8 positive, 5 negative
Customers have mixed opinions about the writing quality of the book, with some finding it well-written while others note issues with editing and formatting, particularly in the Kindle version.
A well-written and practical book for anyone building applications with foundation models....Read more
Poorly edited. Words are frequently hyphenated in nonsensical ways that a trivial editing pass would find and correct....Read more
...The author has an amazing writing style that is fun to read and learn from.Read more
...I have read 50% of this book, I have to say that this book is written so well, each page is very clear, and the reading is fluent...Read more
12 customers mention readability, 7 positive, 5 negative
Customers have mixed opinions about the readability of the book, with some finding it easy-to-read while others report it reads like a summarized essay written by a student.
Fantastic book. Great structure and easy to read. It’s not to learn coding. This is to understand architecture.Read more
A struggle to read, a very dry text....Read more
A dense read, but insightful. Nice work.Read more
This book wasn’t good. It reads like the author’s personal notes: lists of facts at arbitrary levels of detail....Read more
11 customers mention engaging, 7 positive, 4 negative
Customers have mixed opinions about the book's engaging nature, with some finding it fun to read while others find it boring.
...and observability throughout multiple chapters was also refreshing to read as it explored a topic that at the heart of success/failure for AI...Read more
...These issues combine into an unpleasant reading experience where legibility is a constant struggle....Read more
...This one kept me interested throughout the entire book and provided everything clearly....Read more
...Thank you, Author, for creating such an engaging and clear resource. I really like it!Read more
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