Skip to content

Latest commit

 

History

History
53 lines (41 loc) · 2.7 KB

generative-ai-on-aws.md

File metadata and controls

53 lines (41 loc) · 2.7 KB
Raw

Generative AI on AWS

home

Cover Image

Details

  • Title: Generative AI on AWS
  • Subtitle: Building Context-Aware Multimodal Reasoning Applications
  • Authors: Chris Fregly, Antje Barth and Shelbee Eigenbrode
  • Publication Date: 2023
  • Publisher: O'Reilly
  • ISBN-13: 978-1098159221
  • Pages: 309
  • Amazon Rating: 4.4 stars
  • Goodreads Rating: 4.50 stars

Links: Amazon | Goodreads | Publisher | GitHub Project

Blurb

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

Contents

  1. Generative AI Use Cases, Fundamentals, Project Lifecycle
  2. Prompt Engineering and In-Context Learning
  3. Large-Language Foundation Models
  4. Quantization and Distributed Computing
  5. Fine-Tuning and Evaluation
  6. Parameter-efficient Fine Tuning (PEFT)
  7. Fine-tuning using Reinforcement Learning with RLHF
  8. Optimize and Deploy Generative AI Applications
  9. Retrieval Augmented Generation (RAG) and Agents
  10. Multimodal Foundation Models
  11. Controlled Generation and Fine-Tuning with Stable Diffusion
  12. Amazon Bedrock Managed Service for Generative AI