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

Companies are rapidly integrating generative AI into their products and services, but there is much hype and misunderstanding about its impact and promise. This book helps CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology, including the generative AI project life cycle, use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment.

Format: Paperback / softback
Length: 286 pages
Publication date: 26 December 2023
Publisher: O'Reilly Media

Companies are increasingly embracing generative AI to enhance their products and services. However, there is a significant amount of hype and confusion surrounding this technology's impact and potential. In this comprehensive book, authored by Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS, experts guide CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists in discovering practical applications of this exciting new technology.

The book delves into the entire generative AI project life cycle, covering key steps such as use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. It also explores various types of models, including large language models (LLMs) and multimodal models like Stable Diffusion for image generation and Flamingo/IDEFICS for image-related question answering.

By applying generative AI to real-world business use cases, readers can identify the most suitable models for their specific tasks. They will learn how to engineer prompts and perform in-context learning to enhance the performance of generative AI models. The book also discusses fine-tuning generative AI models using low-rank adaptation (LoRA) and aligning them with human values through reinforcement learning from human feedback (RLHF). Retrieval-augmented generation (RAG) is explored as a technique to augment models and enhance their capabilities. Additionally, readers are introduced to libraries like LangChain and ReAct, which facilitate the development of agents and actions for generative AI applications.

With Amazon Bedrock as a foundational platform, the book empowers developers to build robust and scalable generative AI applications. Whether you are a seasoned professional or just starting your journey into this field, this book provides valuable insights and practical techniques to leverage the power of generative AI and drive innovation in your organization.

Weight: 546g
Dimension: 232 x 177 x 19 (mm)
ISBN-13: 9781098159221