Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications

Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications
by 作者: Chris Fregly (Author), Antje Barth (Author), Shelbee Eigenbrode (Author)
Publisher Finelybook 出版社: O’Reilly Media
Edition 版本: 1st
Publication Date 出版日期: 2023-12-19
Language 语言: English
pages 页数: : 309 pages
ISBN-10 书号: 1098159225
ISBN-13 书号: 9781098159221

Book Description

Companies today are moving rapidly to integrate generative AI into their products and services. But there’s a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

You’ll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you’ll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.

  • Apply generative AI to your business use cases
  • Determine which generative AI models are best suited to your task
  • Perform prompt engineering and in-context learning
  • Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA)
  • Align generative AI models to human values with reinforcement learning from human feedback (RLHF)
  • Augment your model with retrieval-augmented generation (RAG)
  • Explore libraries such as LangChain and ReAct to develop agents and actions
  • Build generative AI applications with Amazon Bedrock


“I am very excited about this book. I enjoyed reading it, and I know that you will too! Starting from the basics, you will learn about generative foundation models, prompt engineering, and much more. From there you will proceed to large language models (LLMs) and will see how to use them from within Amazon SageMaker. After you master the basics, you will have the opportunity to learn about multiple types of fine-tuning, and then you will get to the heart of the book and learn to build applications that have the power to perform context-aware reasoning with generative models of different modalities including text and images.”
—Jeff Barr VP and Chief Evangelist at AWS
“This book is a comprehensive resource for building generative AI-based solutions
on AWS. Using real-world examples, Chris, Antje, and Shelbee have done a
spectacular job explaining key concepts, pitfalls, and best practices for LLMs
and multimodal models. A very timely resource to accelerate your journey for
building generative AI solutions from concept to production.”
—Geeta Chauhan, Applied AI Leader at Meta
“This is by far the best book I have come across that makes building generative AI very
practical. Antje, Chris, and Shelbee put together an exceptional resource that will be very
valuable for years—if possible, converted to a learning resource for universities. Definitely
a must-read for anyone building generative AI applications at scale on AWS.”
—Olalekan Elesin, Director of Data Science Platform at HRS Group
“If you’re looking for a robust learning foundation for building and deploying
generative AI products or services, look no further than Generative AI on AWS.
Guided by the deep expertise of authors Chris Fregly, Antje Barth, and Shelbee
Eigenbrode, this book will transition you from a GenAI novice to a master of the
intricate nuances involved in training, fine-tuning, and application development. This
manual is an indispensable guide and true necessity for every budding AI engineer,
product manager, marketer, or business leader.”
—Lillian Pierson, PE, Founder at Data-Mania
“This book goes deep into how GenAI models are actually built and used. And it covers
the whole life cycle, not just prompt engineering or tuning. If you’re thinking about using
GenAI for anything nontrivial, you should read this book to understand what skill sets
and tools you’ll need to be successful.”
—Randy DeFauw, Sr. Principal Solution Architect at AWS
“In the process of developing and deploying a generative AI application, there are
many complex decision points that collectively determine whether the application
will produce high quality output and can be run in a cost-efficient, scalable, and
reliable manner. This book demystifies the underlying technologies and provides
thoughtful guidance to help readers understand and make these decisions, and
ultimately launch successful generative AI applications.”
—Brent Rabowsky, Sr. Manager AI/ML Specialist SA at AWS
“There’s no better book to get started with generative AI. With all the information
on the internet about the topic, it’s extremely overwhelming for anyone. But this
book is a clear and structured guide. It goes from the basics all the way to
advanced topics like parameter-efficient fine-tuning and LLM deployment. It’s also
very practical and covers deployment on AWS too. This book is an extremely
valuable resource for any data scientist or engineer!”
—Alexey Grigorev, Principal Data Scientist at OLX Group
“It’s very rare to find a book that comprehensively covers the full end-to-end process of
model development and deployment! If you’re an ML practitioner, this book is a must!”
—Alejandro Herrera, Data Scientist at Snowflake
“This book is a fantastic end-to-end deep-dive into the Generative AI foundations including how to build enterprise-level Generative AI solutions on AWS. Great work!!”
—Dr. Ramine Tinati, Chief Data Scientist at Accenture
“Generative AI on AWS provides an in-depth look at the innovative techniques for creating applications that comprehend diverse data types and make context-driven decisions. Readers get a comprehensive view, bridging both the theoretical aspects and practical tools needed for Generative AI applications. This book is a must-read for those wanting to harness the full potential of AWS in the realm of Generative AI.”
—Kesha Williams, Director at Slalom Consulting and AWS ML Hero

From the Inside Flap

“Generative AI on AWS” is an exciting and comprehensive book that delves into the rapidly evolving field of generative AI, focusing on its application in various industries and how it can be leveraged using Amazon Web Services (AWS). Authored by Chris Fregly, Antje Barth, and Shelbee Eigenbrode, the book is tailored for a wide range of professionals, including CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists. It provides practical insights and guidelines for utilizing generative AI technologies in business and technical scenarios.
The book covers the entire lifecycle of a generative AI project, beginning with use case definition, model selection, and fine-tuning, to more advanced topics like retrieval-augmented generation, reinforcement learning from human feedback, and model quantization optimization. It also explores various model types, such as large language models (LLMs) and multimodal models like Stable Diffusion and Flamingo/IDEFICS, which are used for image generation and answering questions about images​​.
Designed for AI/ML enthusiasts, data scientists, and engineers, the book assumes a basic understanding of Python and deep learning frameworks such as TensorFlow or PyTorch. Readers will learn about prompt engineering, in-context learning, pretraining of generative models, domain adaptation, model evaluation, and parameter-efficient fine-tuning. The book also introduces tools and platforms like Hugging Face Model Hub, Amazon SageMaker JumpStart, and Amazon Bedrock managed service for generative AI, helping readers get hands-on experience with popular large language models and multimodal generative models​​.
Overall, “Generative AI on AWS” is an indispensable resource for anyone looking to harness the power of generative AI. It not only provides a solid theoretical foundation but also offers practical guidance and examples for implementing these advanced technologies in real-world applications. The book is particularly valuable for those looking to integrate generative AI into their products and services, as it demystifies the technology and offers a clear pathway from concept to production.

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