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

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.

• Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more
• Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot
• Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment
• Tie everything together into a repeatable machine learning operations pipeline
• Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka
• Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more