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 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants

A deployed production model needs to be updated for a variety of reasons, such as to gain access to new training data, to experiment with a new algorithm and hyperparameters, or to model predictive performance deteriorating over time. Any time you update a model with a new version in production, there is a risk of the model becoming unavailable during the update and the model's quality being worse than the previous version. Even after careful evaluation in the development and QA environments, new models need additional testing, validation, and monitoring to make sure they work properly in production.

When deploying new versions of models into production, you should carefully consider reducing deployment risks and minimizing downtime for the model consumers. It is also important to proactively plan for an unsuccessful model update and roll back to a previous working model. Replacing an...