Paperback. 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.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild and scale AI agents using Amazon Bedrock AgentCore and Strands AgentsFine-tune, evaluate, and deploy ML models using Amazon SageMaker AIAutomate LLMOps workflows with SageMaker PipelinesBook DescriptionModern 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.*Email sign-up and proof of purchase required"What you will learnBuild and deploy AI agents using Bedrock AgentCore and Strands AgentsDive deep into ML engineering with Amazon SageMaker AIEvaluate model performance using LLM-as-a-judgeExplore advanced model fine-tuning and deployment using SageMaker AIBuild RAG-powered systems using Bedrock Knowledge Bases and S3 VectorsModernize analytics with a managed transactional data lakeAutomate LLMOps pipelines using SageMaker Pipelines and AWS LambdaExplore best practices for building GenAI systems and AI agents on AWSWho this book is forThis 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. This hands-on book teaches you how to design, build, optimize, and secure generative AI systems and AI agents on AWS. As you progress through the chapters, you'll leverage various AWS services to automate end-to-end LLMOps pipelines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Machine Learning Engineering on AWS (Paperback)
Publisher: Packt Publishing Limited, Birmingham
Publication Date: 2026
Language: English
ISBN 10: 1835881084
ISBN 13: 9781835881088
Binding: Paperback
Condition: new

About this title

Synopsis

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.

Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*

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.

*Email sign-up and proof of purchase required"

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.

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

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.

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