MARC details
| 000 -LEADER |
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| fixed length control field |
03523nam a22003617a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251011020004.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250925b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789355426666 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
IIITD |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.1 |
| Item number |
HUY-A |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Huyen, Chip |
| 245 ## - TITLE STATEMENT |
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| Title |
AI engineering : |
| Remainder of title |
building applications with foundation models |
| Statement of responsibility, etc |
by Chip Huyen |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Sebastopol : |
| Name of publisher, distributor, etc |
O'Reilly Media, |
| Date of publication, distribution, etc |
©2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi, 509 p. : |
| Other physical details |
col. ill. ; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
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| General note |
includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
1. Introduction to Building AI Applications with Foundation Models. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
2. Understanding Foundation Models. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
3. Evaluation Methodology. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
4. Evaluate AI Systems. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
5. Prompt Engineering. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
6. RAG and Agents. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
7. Finetuning. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
8. Dataset Engineering. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
9. Inference Optimization. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
10. AI Engineering Architecture and User Feedback. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Application software -- Development |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence -- Technological innovations |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Software engineering |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha issues (borrowed), all copies |
1 |
