Book Image

Amazon SageMaker Best Practices

By : Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
Book Image

Amazon SageMaker Best Practices

By: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

Overview of this book

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.
Table of Contents (20 chapters)
Section 1: Processing Data at Scale
Section 2: Model Training Challenges
Section 3: Manage and Monitor Models
Section 4: Automate and Operationalize Machine Learning

Chapter 14: Managing SageMaker Features across Accounts

AWS publishes best practices around the management and governance of workloads. These practices touch on many areas, such as cost optimization, security, compliance, and ensuring the operational efficiency of workloads scaled on AWS. Multi-account patterns are one common architectural consideration when building, deploying, and operating workloads that utilize the features of Amazon SageMaker.

In this section, we won't cover the well-established recommendations and considerations around the governance of AWS workloads across AWS accounts. Rather, we will specifically focus on some of the considerations around the usage of AWS features across AWS accounts. For more information about general recommendations for choosing the right account strategy, please refer to AWS Management and Governance services ( and the AWS Multi-Account Landing Zone strategyAWS...