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sendas n definitive guide machine learning operations aws 2024

Type:: E-books
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Uploaded On:: Jan. 10, 2025, 8:37 a.m.
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Textbook in PDF format This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOPS tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps. Machine Learning operations (MLOps) is when DevOps principles are applied to a Machine Learning system. This is a relatively new term as nowadays most businesses try to incorporate AI/ML systems into their products and platforms. MLOps is an engineering discipline that aims to unify ML systems development (dev) and ML systems deployment/operations (ops) to standardize and streamline the continuous delivery of high-performing models in production. MLOps aims to provide high-quality Machine Learning solutions in production in an automated and repeatable manner. MLOps has three contributing disciplines: Machine Learning, DevOps, and data engineering. MLOps is an extension of the DevOps practice of continuously building, deploying code, and testing applied to data engineering (data) and Machine Learning (models). 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 development Catalog ML artifacts centrally for model reproducibility and governance Integrate ML workflows with CI/CD pipelines for faster time to production Continuously monitor data and models in production to maintain quality Optimize model deployment for performance and cost About the Technical Reviewer Foundations of MLOps on AWS Operational Excellence in MLOps MLOps Security in AI/ML MLOps Reliability in AI/ML Performance Efficiency in MLOps Cost Optimization in MLOps MLOps Case Studies Understanding Generative AI Future Trends in MLOps

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Sendas N. The Definitive Guide to Machine Learning Operations in AWS...2024.pdf 15.8 MB

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sendas n definitive guide machine learning operations aws 2024

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