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

Deployment strategies for updating ML models with SageMaker Endpoint Production Variants

In this section, we will dive into multiple deployment strategies you can adopt to update production models using SageMaker Endpoint Production Variants. While some deployment strategies are easy to implement and are cost-effective, others add complexity while lowering deployment risks. We will dive into five different strategies, including Standard, A/B, Blue/Green, Canary, and Shadow deployments, and discuss the various steps involved in each approach.

Standard deployment

This strategy is the most straightforward approach to deploying and updating models in production. In a Standard model deployment, there is always a single active SageMaker endpoint, and the endpoint is configured with a single production variant, which means only a single model is deployed behind the endpoint. All inference traffic is processed by a single model. The endpoint configuration is similar to Endpoint Configuration...