03458nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001300153050001400166072001600180072002300196072001500219100007500234245017900309250001800488264005400506300006900560336002600629337002600655338003600681347002400717505039900741520146201140650002902602650002102631650002702652650002902679650002102708650002702729700007802756710003402834773002602868776003602894776003602930856004602966979-8-8688-1076-3DE-He21320251030114920.0cr nn 008mamaa250104s2024    xxu|    s    |||| 0|eng d  a97988688107637 a10.1007/979-8-8688-1076-32doi 4aQ334-342 4aTA347.A78 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aSendas, Neel.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut14aThe Definitive Guide to Machine Learning Operations in AWSh[electronic resource] :bMachine Learning Scalability and Optimization with AWS /cby Neel Sendas, Deepali Rajale.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXVIII, 440 p. 128 illus., 127 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1: Introduction to MLOps -- Chapter 2: Foundations of MLOps on AWS -- Chapter 3: Operational Excellence in MLOps -- Chapter 4: Security in MLOps -- Chapter 5: Reliability in MLOps -- Chapter 6: Performance Efficiency in MLOps -- Chapter 7: Cost Optimization in MLOps -- Chapter 8 MLOps Best Practices and Case Studies -- Chapter 9: MLOps for GenAI -- Chapter 10: Future Trends in MLOps.  aThis book focuses on deploying, testing, monitoring, and automating ML systems in production at Cloud scale. It covers AWS MLOps services such as 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 Framework. 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 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. 0aArtificial intelligence. 0aCloud Computing. 0aProduction management.14aArtificial Intelligence.24aCloud Computing.24aOperations Management.1 aRajale, Deepali.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886881075608iPrinted edition:z979886881077040uhttps://doi.org/10.1007/979-8-8688-1076-3