Learn to solve relevant machine learning engineering challenges when building Generative AI applications on AWS and automate the LLMOps workflow using AWS services like Amazon Bedrock and Amazon SageMaker Key Features Build RAG and agent-based Gen AI apps with AWS services. Leverage Amazon Bedrock for secure, responsible AI, and next-gen Amazon SageMaker for data, analytics, and ML engineering. Apply access controls, compliance features, and best practices to ensure robust ML system security Book DescriptionThe recent advancements in generative AI, large language models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents have accelerated the demand for machine learning engineers capable of building, managing, and scaling modern AI-powered systems. As the landscape of AI rapidly evolves, staying ahead requires a deep understanding of the relevant concepts as well as the practical tools, services, and platforms needed to implement them effectively. With this hands-on book, you will discover how to leverage various AWS services such as Amazon Bedrock and the next generation of Amazon SageMaker to build, optimize, and manage production-ready machine learning (ML) systems. You will learn how to build RAG-powered Generative AI applications, automate LLMOps workflows, build reliable and responsible AI agents, optimize a managed transactional data lake, and make use of proven deployment and evaluation strategies when dealing with various models. To help elevate your expertise on ML engineering, each chapter includes practical examples and clear explanations to help you manage, troubleshoot, and optimize ML systems running on AWS. By the end of this book, you'll be able to operationalize and secure Generative AI applications on AWS, which will give you the confidence needed to solve a wide variety of ML engineering requirements.What you will learn Leverage model distillation techniques to build cost-efficient models Learn how to build RAG and agent-based generative AI applications Leverage fully managed Apache Iceberg tables with Amazon S3 tables Automate production-ready end-to-end machine learning pipelines on AWS Monitor models, data, and infrastructure to detect potential issues Apply proven cost optimization techniques for Generative AI systems Who this book is forThis book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to learn more about Machine Learning Engineering, Generative AI, Large Language Models, Retrieval-Augmented Generation, AI Agents, and MLOps on AWS. Readers will be equipped with the knowledge needed to build, manage, scale, and secure production-ready machine learning systems on AWS that power Generative AI applications. The reader is expected to have a basic understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts.

Table of Contents:
Table of Contents A Gentle Introduction to Generative AI on AWS Exploring the High-Level AI/ML services of AWS Machine Learning Engineering with Amazon SageMaker Practical Data Management on AWS Pragmatic Data Processing and Analysis Getting Started with SageMaker Training Solutions Diving Deeper into SageMaker Training Solutions Model Evaluation, Benchmarking, and Bias Detection Machine Learning Model Deployment on AWS Machine Learning Model Deployment Strategies Model Monitoring & Management Solutions Security, Governance, and Compliance Strategies Machine Learning Pipelines with SageMaker Pipelines Part I Machine Learning Pipelines with SageMaker Pipelines Part II