Generative AI on AWS: Building Context-Aware

Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications. Chris Fregly, Antje Barth, Shelbee Eigenbrode

Generative-AI-on-AWS-Building.pdf
ISBN: 9781098159221 | 151 pages | 4 Mb

• Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications
• Chris Fregly, Antje Barth, Shelbee Eigenbrode
• Page: 151
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781098159221
• Publisher: O'Reilly Media, Incorporated

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

Ebooks gratis download nederlands Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications 9781098159221 CHM (English literature) by Chris Fregly, Antje Barth, Shelbee Eigenbrode

Overview

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

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