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

Examining multi-account considerations with Amazon SageMaker

In this section, we'll cover multi-account considerations with Amazon SageMaker. We'll first look at a general reference architecture, then discuss some of the considerations for specific SageMaker features across the ML Lifecycle.

Figure 14.2 shows an example of a multi-account structure mapping key SageMaker features and other common AWS services to the accounts they are typically used in. This is not a one-size-fits-all view, as there may be other AWS services or third-party tools that are performing one or more of the functions performed by the AWS services shown. As an example, your model registry may be the SageMaker model registry, or it could alternatively be Amazon DynamoDB or a tool such as MLflow:

Figure 14.2 – Example of service use across AWS accounts

The placement of the AWS, or equivalent, supporting the ML Lifecycle map to the phase, model build, or model deploy...