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

by Joshua Arvin Lat

Overview

Solve machine learning engineering challenges for GenAI applications on AWS and automate the LLMOps workflows using AWS services like Amazon Bedrock and Amazon SageMaker

Key Features

• Learn how to build RAG and agent-based GenAI 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
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Recent advancements in generative AI, large language models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents have created a soaring demand for machine learning engineers who can build, manage, and scale modern AI-powered systems. To stay ahead in this rapidly evolving AI landscape, you need a deep theoretical understanding as well as hands-on expertise with the right tools, services, and platforms. Machine Learning Engineering on AWS is a practical guide that teaches you how to harness AWS services such as Amazon Bedrock and the next generation of Amazon SageMaker to build, optimize, and manage production-ready ML systems. You’ll learn how to build RAG-powered GenAI applications, automate LLMOps workflows, develop reliable and responsible AI agents, and optimize a managed transactional data lake. The book also covers proven deployment and evaluation strategies for dealing with various models, along with practical examples to help you manage, troubleshoot, and optimize ML systems running on AWS. Guided by AWS Machine Learning Hero Joshua Arvin Lat, you’ll be able to grasp complex ML concepts with clarity and gain the confidence to operationalize and secure GenAI applications on AWS to meet a wide variety of ML engineering requirements.

What you will learn

• Implement model distillation techniques to build cost-efficient models
• Develop 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 for

This book is for AI engineers, data scientists, machine learning engineers, and technology leaders who want to learn more about machine learning engineering, GenAI, LLMs, RAG, AI agents, and MLOps on AWS. A basic understanding of artificial intelligence, machine learning, generative AI, and cloud engineering concepts is a must.

Product Details

ISBN-13:	9781835881088
Publisher:	Packt Publishing
Publication date:	08/29/2025
Product dimensions:	75.00(w) x 92.50(h) x (d)

About the Author

Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO for three Australian-owned companies and as director of software development and engineering for multiple e-commerce start-ups in the past. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.

Table of Contents

Table of Contents

1. A Gentle Introduction to Generative AI on AWS
2. Exploring the High-Level AI/ML services of AWS
3. Machine Learning Engineering with Amazon SageMaker
4. Practical Data Management on AWS
5. Pragmatic Data Processing and Analysis
6. Getting Started with SageMaker Training Solutions
7. Diving Deeper into SageMaker Training Solutions
8. Model Evaluation, Benchmarking, and Bias Detection
9. Machine Learning Model Deployment on AWS
10. Machine Learning Model Deployment Strategies
11. Model Monitoring and Management Solutions
12. Security, Governance, and Compliance Strategies
13. Machine Learning Pipelines with SageMaker Pipelines Part I
14. Machine Learning Pipelines with SageMaker Pipelines Part II