COMING SOON. Publishing in Feb 2025
$19.99
per month
Paperback
Feb 2025
2nd Edition
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Leverage AWS services to solve relevant machine learning engineering challenges for Generative AI
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Automate the LLMOps pipeline using Amazon SageMaker along with other AWS services and features
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Bridge the gap between theory and practice when managing deep learning experiments and deployments
The 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.
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, 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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Solve relevant ML engineering challenges when building Gen AI systems
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Automate the LLMOps pipeline using various AWS services and features
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Manage and optimize deep learning experiments and deployments on AWS
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Explore relevant patterns and anti-patterns when deploying LLMs
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Build Gen AI applications powered by retrieval-augmented generation
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Apply proven cost optimization techniques for Generative AI systems