03846nam a22005295i 4500 979-8-8688-1076-3 DE-He213 20251030114920.0 cr nn 008mamaa 250104s2024 xxu| s |||| 0|eng d 9798868810763 979-8-8688-1076-3 10.1007/979-8-8688-1076-3 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Sendas, Neel. author. aut http://id.loc.gov/vocabulary/relators/aut 90164 The Definitive Guide to Machine Learning Operations in AWS [electronic resource] : Machine Learning Scalability and Optimization with AWS / by Neel Sendas, Deepali Rajale. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XVIII, 440 p. 128 illus., 127 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 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. This 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. Artificial intelligence. 58441 Cloud Computing. 58386 Production management. 67141 Artificial Intelligence. 58441 Cloud Computing. 58386 Operations Management. 67304 Rajale, Deepali. author. aut http://id.loc.gov/vocabulary/relators/aut 90165 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868810756 Printed edition: 9798868810770 https://doi.org/10.1007/979-8-8688-1076-3 ZDB-2-CWD ZDB-2-SXPC 66342 66342