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April 22, 2026, 4:55 p.m.
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(Last updated: April 22, 2026, 4:55 p.m.)
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| ['Huyen C. AI Engineering. Building Applications with Foundation Models 2025.pdf'] | 0 bytes |
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NOTE
SOURCE: Huyen C. AI Engineering. Building Applications with Foundation Models 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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). 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 works in the intersection of AI, data, and storytelling. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup (acquired), worked on GPU optimization for data processing, and taught Machine Learning Systems Design at Stanford. Her last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages
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