Machine Learning Engineering on AWS: Operationalize and optimize Generative AI systems and LLMOps pipelines in production

Solve machine learning engineering challenges for GenAI-powered systems and AI agents on AWS, and automate LLMOps pipelines using Amazon Bedrock, SageMaker AI, Bedrock AgentCore, and Strands Agents.

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Key Features:

- Build and scale AI agents using Amazon Bedrock AgentCore and Strands Agents

- Fine-tune, evaluate, and deploy ML models using Amazon SageMaker AI

- Automate LLMOps workflows with SageMaker Pipelines

Book Description:

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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What You Will Learn:

- Build and deploy AI agents using Bedrock AgentCore and Strands Agents

- Dive deep into ML engineering with Amazon SageMaker AI

- Evaluate model performance using LLM-as-a-judge

- Explore advanced model fine-tuning and deployment using SageMaker AI

- Build RAG-powered systems using Bedrock Knowledge Bases and S3 Vectors

- Modernize analytics with a managed transactional data lake

- Automate LLMOps pipelines using SageMaker Pipelines and AWS Lambda

- Explore best practices for building GenAI systems and AI agents on AWS

Who this book is for:

This book is intended for AI engineers, data scientists, machine learning engineers, and technology leaders who want to deepen their understanding of machine learning engineering, generative AI, large language models, retrieval-augmented generation, AI agents, and MLOps on AWS. A foundational understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is recommended.

Table of Contents

- A Gentle Introduction to Generative AI and AI Agents on AWS

- Building AI Agents with SageMaker AI and Bedrock AgentCore

- Machine Learning Engineering with Amazon SageMaker AI

- Modernizing Analytics with a Managed Transactional Data Lake

- Practical Data Management on AWS

- Pragmatic Data Processing on AWS

- SageMaker AI Model Training and Tuning Capabilities

- SageMaker AI Model Deployment Options and Strategies

- Automating LLMOps Workflows with SageMaker Pipelines