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

Joshua Arvin Lat

Softcover

ISBN 10: 1835881084 ISBN 13: 9781835881088

Verlag: Packt Publishing Limited, 2026

Inhaltsangabe

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 Description

The 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 for

This 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

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 & 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

Über diesen Titel

Über die Autorin bzw. den Autor

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.

Details zur Ausgabe

• Verlag: Packt Publishing Limited
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 1835881084
• ISBN 13: 9781835881088
• Einband: Taschenbuch
• Auflage: 2
• Anzahl der Seiten: 470
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Nicht verfügbar