Name: Ai Engineering: Building Applications With Foundation Models
SKU: 63533671
Price: 2040.00 TWD
Availability: InStock
Author: Chip Huyen

portada Ai Engineering: Building Applications With Foundation Models

Ai Engineering: Building Applications With Foundation Models

Type

Physical Book

Author

Chip Huyen

Publisher

O'reilly Media

Language

English

Pages

532

Format

Paperback

ISBN13

9781098166304

Edition No.

Ai Engineering: Building Applications With Foundation Models

Chip Huyen (Author) · O'reilly Media · Paperback

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Synopsis "Ai Engineering: Building Applications With Foundation Models"

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).

Chip Huyen

(Author)

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Chip Huyen es una escritora, ingeniera y emprendedora vietnamita-estadounidense reconocida por su trabajo en inteligencia artificial, aprendizaje automático y sistemas en tiempo real. Nacida en Vietnam, se trasladó a Estados Unidos para continuar sus estudios y ha construido una carrera destacada que combina la tecnología con la comunicación clara y accesible de ideas complejas.

Es graduada de la Universidad de Stanford, donde se especializó en informática. Durante su tiempo allí, trabajó en proyectos de aprendizaje profundo y aprendizaje automático, y también fue profesora asistente para cursos de inteligencia artificial aplicada. Ha trabajado en empresas de alto perfil como NVIDIA, Snorkel AI y Netflix, donde se enfocó en el desarrollo de sistemas de ML escalables y en tiempo real.

Además de su carrera técnica, Chip Huyen es autora del libro Designing Machine Learning Systems, una guía práctica para ingenieros y científicos de datos que buscan implementar sistemas de ML en producción. También ha escrito libros y artículos en vietnamita, y es conocida por su habilidad para enseñar conceptos técnicos de manera clara.

En años recientes, cofundó una startup enfocada en infraestructura para aprendizaje automático en tiempo real. Su trabajo representa la intersección entre investigación, ingeniería aplicada y comunicación técnica.

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