The Definitive Guide to Machine Learning Operations in AWS: Machine Learning Scalability and Optimization with AWS

В этом материале разберём тему: mlops.

It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps

By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS.

What you will learn:Create repeatable training workflows to accelerate model developmentCatalog ML artifacts centrally for model reproducibility and governanceIntegrate ML workflows with CI/CD pipelines for faster time to productionContinuously monitor data and models in production to maintain qualityOptimize model deployment for performance and costWho this book is for:This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.

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