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

Understanding the benefits of using multiple AWS accounts with Amazon SageMaker

In this section, we'll cover the general, high-level benefits of using multiple AWS accounts. We'll also discuss the considerations that are specific to using Amazon SageMaker across the ML Lifecycle:

  • Benefit #1: Implementing specific security controls

    Using multiple AWS accounts allows customers to implement security controls that are specific to the workload, environment, or data. As an example, some workloads may have unique security requirements (such as PCI compliance) and require additional controls. Using multiple accounts allows you to maintain fine-grained controls that are isolated and auditable at the AWS account level.

    For the model-building activities included in the ML Lifecycle, using multiple AWS accounts allows you to create and manage data science environments that include the controls that are specific to machine learning, as well as to your security requirements. With...