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

Chapter 8: Managing Models at Scale Using a Model Registry

As you begin to deploy multiple models and manage multiple model versions, ensuring core architectural practices such as governance, traceability, and recoverability are followed is challenging without using a model registry. A model registry is a central store containing metadata specific to a model version. It includes information on how the model was built, the performance of that model, as well as where and how the model is deployed. Model registry services or solutions often include additional capabilities, such as approval workflows and notifications.

In this chapter, we'll cover the concept of a model registry and why a model registry is important for managing multiple models at scale. We'll also outline considerations you need to make when choosing a model registry implementation, in order to best meet the needs of your environment and operational requirements. For this, we'll examine two example...