Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Chris Fregly is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS) based in San Francisco, California. Chris holds every AWS certification. He is also co-founder of the global Generative AI on AWS Meetup. Chris regularly speaks at AI and Machine Learning meetups and conferences across the world. Previously, Chris was an engineer at Databricks and Netflix where he worked on scalable big data and machine learning products and solutions. He is also co-author of the O'Reilly book, Data Science on AWS.
Antje Barth is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. She is also co-founder of the global Generative AI on AWS Meetup. Antje frequently speaks at AI and machine learning conferences and meetups around the world, including the O'Reilly AI and Strata conferences. Besides Generative AI, Antje is passionate about helping developers leverage big data, containers, and Kubernetes platforms in the context of AI and Machine Learning. Prior to joining AWS, Antje worked in technical evangelist and solutions engineering roles at MapR and Cisco. She is also co-author of the O'Reilly book, Data Science on AWS.
Shelbee Eigenbrode is a Principal Solutions Architect for Generative AI at Amazon Web Services (AWS) based in Denver, Colorado. She is co-founder of the Denver chapter of Women in Big Data. Shelbee holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She focuses on combining her DevOps and ML backgrounds to deliver ML workloads at scale. With over 35 patents granted across various technology domains, Shelbee has a passion for continuous innovation and using data to drive business outcomes.
From the Back Cover
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.
High-level topics
- 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
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.
"About this title" may belong to another edition of this title.
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