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Machine Learning Engineering on AWS : Build, Scale, and Secure Machine Learning Systems and MLOps Pipelines in Production by Joshua Arvin Lat is a nonfiction available to read on EtoBox.

What is Machine Learning Engineering on AWS : Build, Scale, and Secure Machine Learning Systems and MLOps Pipelines in Production about?

Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key Features Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more Use container and serverless services to solve a variety of ML engineering requirements Design, build, and secure automated MLOps pipelines and workflows on AWS Book Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to

Who reads Machine Learning Engineering on AWS : Build, Scale, and Secure Machine Learning Systems and MLOps Pipelines in Production?

Author

Joshua Arvin Lat

Publisher

Packt Publishing, Limited

Published

2022

Language

EN

ISBN

9781803231389

Category

nonfiction

Subjects

Science, Computer Science, Stem