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

Deploying multiple models behind a single inference endpoint

A SageMaker inference endpoint is a logical entity that actually holds a load balancer and one or more instances of your inference container. You can deploy either multiple versions of the same model or entirely different models behind a single endpoint. In this section, we'll look at these two use cases.

Multiple versions of the same model

A SageMaker endpoint lets you host multiple models that serve different percentages of traffic for incoming requests. That capability supports common continuous integration (CI)/continuous delivery (CD) practices such as canary and blue/green deployments. While these practices are similar, they have slightly different purposes, as explained here:

  • A canary deployment means that you let the new version of a model host a small percentage of traffic that lets you test a new version of the model on a subset of traffic until you are satisfied that it is working well.
  • A...