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 an overview of the AWS multi-account environment

There are many variations of multi-account strategies that are valid. Multi-account implementations can vary based on the organizational and technical needs of a customer. For the purposes of this chapter, we will focus on a basic multi-account strategy, focusing on only the accounts that are most relevant to a machine learning workload using Amazon SageMaker. We don't explicitly call out accounts (such as security or logging) because they are already well defined in the context of AWS governance practices. Figure 14.1 illustrates the general, high-level accounts we will use to discuss the concepts in this chapter.

Figure 14.1 – Example of AWS accounts and SageMaker features

Using Figure 14.1 as an example, the following AWS accounts may be used as part of an end-to-end ML Lifecycle. Please keep in mind that account naming and resource placement may vary considerably across implementations...