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

Choosing a model registry solution

There are multiple options available for implementing a model registry. While each implementation offers different features or capabilities, the concept of providing a central repository to track key metadata largely remains the same across implementations. In this section, we'll cover a few common patterns for creating a model registry, as well as discuss the considerations for each. The patterns covered in this section include the following:

  • Amazon SageMaker model registry
  • Building a custom model registry
  • Utilizing a third-party or open source software (OSS) model registry

Amazon SageMaker model registry

The Amazon SageMaker model registry is a managed service that allows you to centrally catalog models, manage model versions, associate metadata with your model versions, and manage the approval status of a model version. The service is continuously evolving with new features, so the information contained in this...