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Table Of Contents
Machine Learning Engineering on AWS - Second Edition
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Machine Learning Engineering on AWS
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Overview of this book
Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.
Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.
AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.
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Table of Contents (12 chapters)
Preface
Free Chapter
Chapter 1: A Gentle Introduction to Generative AI and AI Agents on AWS
Chapter 2: Building AI Agents with SageMaker AI and Bedrock AgentCore
Chapter 3: Machine Learning Engineering with Amazon SageMaker AI
Chapter 4: Modernizing Analytics with a Managed Transactional Data Lake
Chapter 5: Practical Data Management on AWS
Chapter 6: Pragmatic Data Processing on AWS
Chapter 7: SageMaker AI Model Training and Tuning Capabilities
Chapter 8: SageMaker AI Model Deployment Options and Strategies
Chapter 9: Automating LLMOps Workflows with SageMaker Pipelines
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Index
