000			03613nam a22003257a 4500
001			45019
003			BD-DhAAL
005			20251110160210.0
008			251110t2025 ii a b 001 0 eng d
020			_a9789355426666 (softcover)
040			_aBD-DhAAL _cBD-DhAAL
082			_a005.1 _223
100			_aHuyen, Chip, _eauthor. _959791
245			_aAI engineering : _bbuilding applications with foundation models / _cChip Huyen.
246	3	8	_aAI engineering
260			_aNavi Mumbai : _bShroff Publishers and Distributors ; _aSebastopol, CA : _bO'Reilly Media, _cc2025 [reprinted].
300			_axxi, 509 pages : _bcolor illustrations ; _c23 cm
500			_aFirst edition : December 2024
504			_aIncludes bibliographical references and index.
520			_aRecent 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).
526			_aCSE
650			_aSoftware engineering.
650			_aArtificial intelligence _xTechnological innovations. _959808
650			_aApplication software _xDevelopment.
650			_aMachine learning.
650			_aComputer science.
852			_aAyesha Abed Library _cGeneral Stacks
942			_cBK _2ddc
999			_c47624 _d47624