The Definitive Guide to Machine Learning Operations in AWS
Apress (Verlag)
979-8-8688-1075-6 (ISBN)
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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
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
Who this book is for:
This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.
Neel Sendas is a Principal Technical Account Manager at Amazon Web Services (AWS). In this role, he serves as the AWS Cloud Operations lead for some of the largest enterprises that utilize AWS services. Drawing from his expertise in cloud operations, in this book, Neel presents solutions to common challenges related to ML Cloud Governance, Cloud Finance, and Cloud Operational Resilience & Management at scale. Neel also plays a crucial role as part of the core team of Machine Learning Technical Field Community leaders at AWS, where he contributes to shaping the roadmap of AWS Artificial Intelligence and Machine Learning (AI/ML) Services. Neel is based in the state of Georgia, United States. Deepali Rajale is a former AWS ML Specialist Technical Account Manager, with extensive experience supporting enterprise customers in implementing MLOps best practices across various industries. She is also the founder of Karini AI, a company dedicated to democratizing generative AI for businesses. She enjoys blogging about ML and Generative AI and coaching customers to optimize their AI/ML workloads for operational efficiency and cost optimization. In her spare time, she enjoys traveling, seeking new experiences, and keeping up with the latest technology trends.
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.
| Erscheinungsdatum | 08.01.2025 |
|---|---|
| Zusatzinfo | 127 Illustrations, color; 1 Illustrations, black and white; XVIII, 440 p. 128 illus., 127 illus. in color. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Software Entwicklung ► SOA / Web Services | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | AWS • Cost Optimization in MLOps • machine learning • MLOps • MLOPS for GenAI • Operational Excellence in MLOps • Reliability in MLOps • Security in MLOps |
| ISBN-13 | 979-8-8688-1075-6 / 9798868810756 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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