Generative AI on AWS (Early Release) by Antje Barth, Chris Fregly, Shelbee Eigenbrode 9781098159184 1098159187

Generative AI on AWS (Early Release). Antje Barth, Chris Fregly, Shelbee Eigenbrode. – Ebook Instant Download/Delivery ISBN(s): 9781098159214, 1098159217, 9781098159184, 1098159187

Product details:

• ISBN 10: 1098159187
• ISBN 13: 9781098159184
• Author: Antje Barth, Chris Fregly, Shelbee Eigenbrode

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

Table contents:

1. Generative AI Use Cases, Fundamentals, and Project Life Cycle

2. Prompt Engineering and In-Context Learning

3. Large-Language Foundation Models

4. Memory and Compute Optimizations

5. Fine-Tuning and Evaluation

6. Parameter-Efficient Fine-Tuning

7. Fine-Tuning with Reinforcement Learning from Human Feedback

8. Model Deployment Optimizations

9. Context-Aware Reasoning Applications Using RAG and Agents

10. Multimodal Foundation Models

11. Controlled Generation and Fine-Tuning with Stable Diffusion

12. Amazon Bedrock: Managed Service for Generative AI

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