博客來GenAI is one of the most transformative technologies of our time, fundamentally changing how we build software and solve business problems. Unlike traditional AI that analyzes data to make predictions or classifications and usually requires training separate models for each specific task, GenAI provides powerful pre-trained models that can generate entirely new content from text and code to images and audio, while understanding context and responding to diverse prompts without additional training.
This book is your comprehensive guide to navigating AWS’s complete GenAI ecosystem and building intelligent applications that deliver real business value. It covers the spectrum of AWS GenAI services like Amazon Bedrock for managed foundation models, Amazon SageMaker JumpStart for flexible ML development, and Amazon Q for business and developer productivity. You will learn essential techniques from prompt engineering and systematic model evaluation to advanced implementations like RAG with Knowledge Bases that enhance responses with proprietary data. The book then advances to autonomous agents that can reason, plan, and execute complex workflows using both Amazon Bedrock’s managed agents and open-source agentic AI frameworks.
By the end of this book, you will have the knowledge, architectural patterns, and strategies needed to build GenAI solutions on AWS, whether you are just getting started with GenAI or building enterprise-scale multi-agent systems.
WHAT YOU WILL LEARN
● Implement real-world applications using Amazon Bedrock, Amazon SageMaker JumpStart, and Amazon Q.
● Implement advanced techniques like RAG, fine-tuning, and agentic AI.
● Master prompt engineering techniques and best practices.
● Develop secure and compliant GenAI solutions.
● Learn AWS best practices and responsible AI principles.
WHO THIS BOOK IS FOR
This book is designed for software developers, AI/ML engineers, and solutions architects who want to build practical GenAI applications on AWS. It is ideal for developers with basic Python programming experience, a cloud engineer familiar with AWS services, or a technical professional transitioning into AI/ML roles.
