AI Engineering: Building Applications with Foundation Models (Full Colour Indian Edition)
Paperback
• 536 Pages
• ₹ 2500.00
• English
• 9789355426666
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| Publisher | Shroff/O’Reilly |
|---|---|
| ISBN13 | 9789355426666 |
| ASIN/SKU | 9355426666 |
| Book Format | Paperback |
| Language | English |
| Pages | 536 |
| List Price | ₹ 2500.00 |
| Publishing Date | 19/12/2024 |
| Dimensions | 22.86 x 17.78 x 2.54 cm |
| Weight | 900 g |
| Book Code | BD00069531 |
Discover AI Engineering: Building Applications with Foundation Models (Full Colour Indian Edition) by Chip Huyen. This book is published by Shroff/O’Reilly in Paperback format, ISBN 9789355426666, ASIN 9355426666, under Engineering and Transportation, Artificial Intelligence, Computer Science Books.
Book Description
Shroff Publishers do not endorse the preview pages of kindle linked to our ISBNs. All Indian Reprints of O'Reilly are Printed in Grayscale.
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.
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
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.
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
Author Biography
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.
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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