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

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

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

by Joshua Arvin Lat

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Paperback / softback. New. Learn to effectively solve relevant machine learning engineering challenges when building Generative AI applications on AWS. Automate the LLMOps pipeline using various AWS services and solutions to streamline the ML workflow. Key Features Leverage AWS services to solve relevant machine learning engineering challenges for Generative AI Automate the LLMOps pipeline using Amazon SageMaker along with other AWS services and features Bridge the gap between theory and practice when managing deep learning experiments and deployments Book DescriptionThe widespread of solutions powered by generative AI and large language models has led to a surge in the demand for machine learning engineers capable of building, managing, and scaling complex Gen AI-powered applications and systems. This book starts by introducing relevant concepts such as Machine Learning Engineering, Generative AI, Large Language Models (LLMs), and MLOps. As you progress through each of the chapters of the book, you will learn how to leverage these concepts as well as various AWS services and solutions to build, manage, and optimize machine learning systems. In addition to this, you'll discover how to automate the LLMOps pipeline, optimize deep learning experiments, and make use of proven deployment strategies when dealing with LLMs. You'll also learn how to create Gen AI applications powered by retrieval-augmented generation (RAG). To help expand your knowledge and elevate your expertise on machine learning engineering, each chapter includes practical examples and clear explanations to help you manage, troubleshoot, and optimize Generative AI 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 experience and confidence needed for solving a wide variety of ML engineering challenges and requirements.What you will learn Solve relevant ML engineering challenges when building Gen AI systems Automate the LLMOps pipeline using various AWS services and features Manage and optimize deep learning experiments and deployments on AWS Explore relevant patterns and anti-patterns when deploying LLMs Build Gen AI applications powered by retrieval-augmented generation 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, 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.
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Details

  • Title Machine Learning Engineering on AWS: Operationalize and optimize Generative AI systems and LLMOps pipelines in production
  • Author Joshua Arvin Lat
  • Binding Paperback
  • Condition New
  • Bookseller's Inventory # B9781835881088
  • ISBN 9781835881088
  • Quantity available 10

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From the publisher

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.

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

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