AI Engineering: Building Applications with Foundation Models
Original price was: $79.99.$19.99Current price is: $19.99.
✔️ (PDF) • Pages : 535
Sample: https://drive.google.com/uc?export=download&id=1JiDskDUIJRVxO_sDDPJERxMYIcMbhAOi
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 needsChip 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).
19 reviews for AI Engineering: Building Applications with Foundation Models
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Shawn M. Sullivan (verified owner) –
Provides a great balance of the contextual and historical background regarding the evolution/maturation of AI application building coupled with the practical steps and approach to building them on top of foundational models.
Chris Ho (verified owner) –
This is a great book—easy to understand and very enjoyable! Thank you, Author, for creating such an engaging and clear resource. I really like it!
Luis Gutierrez (verified owner) –
I am a PM and finished reading this book- which I have access to through O’Reilly platform. My motivation to read this book stems from wanting to understand Ai, and not the hype that has been going around.
This book provided an excellent intro that takes the reader from where they are, to understanding what Ai can deliver. The author also went as far as doing market research- which helps understand the real opportunity in Ai- using foundational models ( and helps us understand why LLM isn’t a good term 🙂 ).
The book goes in depth in many topics- and overall, gives the reader the understanding they need to be competitive and build real world applications.
I recommend this book to anyone who wants to get an understanding of where we are, where we’re going, and more importantly, how to capotlize by building real features.
Now when my coworkers say they want to build LLM features, I can guide them through the Journey.
My opinion- get this book for beginner and intermediate Ai.
Louis Yi (verified owner) –
Chip Huyen’s AI Engineering is a must-read for anyone thinking about deploying artificial intelligence in real-world applications. She is able to break down key topics into bite-size chunks and give digestible examples that clearly show the fundamentals.
As someone who is new to the field, I appreciate that she covers the full range of topics from the inner workings of foundational models to examples of building an application up from scratch (with all its complexities and pitfalls). And though a wide range of topics are covered, she still is able to provide a great amount of details and depth focusing on the fundamental principles which should stay relevant even in the fast-paced development of AI. Reading through the book, I often found myself jumping between sections to create new and interesting understandings/realizations.
In short, this book makes using AI more accessible and helps to peel through the many layers of AI for those experienced and new.
Michael (verified owner) –
I loved her last book (Designing Machine Learning Systems) so my expectations were pretty high. This book delivers. Even though I’m already familiar with many of the topics, Chip brought fresh new perspectives. I’m giving it to my team to read.
Mary C (verified owner) –
Good beginner book for an emerging field. Worth noting that AI Engineering, as a field, is still in its early days. This book give a gentle but broad view on this field; don’t expect to read bleeding edge advancements here but more of a groundwork of the basics. I very much like how this book places a heavy emphasis on evals, which a lot of current AI Engineering practitioners don’t nearly pay enough attention to.
My only nit is with some of the exposition: The author, on one page, holds your hand explaining what an embedding is while a couple pages further shows us what a transformer block looks like. My question is: What concepts should the reader be expected to absorb?
In any case, very good, foundational laying book.
Isaac Ireland (verified owner) –
Chip has summarised the past few years of rapid development in a concise and understandable format. Perfect for any data specialist.
Denise Shekerjian (verified owner) –
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)
benedikt (verified owner) –
great book
SEÑORA M (verified owner) –
I have been out of loop on LLM work since I joined a new role as a software engineer. Recently, I picked up some side projects that involve tuning LLM outputs, and this book was a great resource to quickly brush up on the best practices for prompt engineering, what to expect in results etc. In particular, along with best practicies, there are a bunch of useful and easy to understand experiments in the book which helped with the intuition
Danish (verified owner) –
Perfect for beginners and intermediate python devs looking to transition into AI engineering.
David Schneider (verified owner) –
I’m only up to Chapter 4, and this book is fantastic! I’m coming from a ML/deep learning background, looking to level up with generative AI and LLM’s, and I think this book is great! I was hesitant at first, there is so much to find for free – but this book is concisely pulling it together with many interesting details! If I had been bouncing around on the web instead of reading this book, I don’t think I’d know 1/2 as much about these early chapter topics as I do now! The book is getting me excited about working in the field – building an AI based product, leveraging existing models, digging into foundation model training – and there’s still 7 more chapters to go!
Tiago O Amaral (verified owner) –
Excelent book! Covers the most important challenges and topics for AI Engineering
Constantin Gonciulea (verified owner) –
The standard for terminology and conceptual understanding.
Brandon Repa (verified owner) –
Awesome book. The author thoroughly explained every topic discussed. Being in a masters program now this book goes even deeper into some of the trends and algorithms than my professors.
Ananta Paine (verified owner) –
Great Book.
Arunkumar (verified owner) –
Good booj
Ralcanta (verified owner) –
Excelente libro,actualizado a la época que estamos viviendo y con conocimientos prácticos.