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

Considerations for automating your SageMaker ML workflows

In this section, we'll review a typical ML workflow that includes the basic steps for model building and deploy activities. Understanding the key SageMaker inputs and artifacts for each step is important in building automated workflows, regardless of the automation or workflow tooling you choose to employ.

This information was covered in Chapter 8, Manage Models at Scale Using a Model Registry. Therefore, if you have not yet read that chapter it's recommended to do so prior to continuing with this chapter. We'll build on that information and cover high-level considerations and guidance for building out automated workflows and CI/CD pipelines for SageMaker workflows. We'll also briefly cover the common AWS native service options when building automated workflows and CI/CD ML pipelines.

Typical ML workflows

An ML workflow contains all the steps required to build an ML model for an ML use case, followed...