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

End-to-end architectures for monitoring ML models

In this section, you will put together the four high-level steps of monitoring to build end-to-end architectures for data drift, model quality, bias drift, and feature attribution drift monitoring. Along with the architecture, you will dive into the unique aspects of the individual steps as applicable to each type of monitoring.

For all four types of monitoring, the first and last steps – enabling data capture and analyzing monitoring results – remain the same. We will discuss these two steps in detail for the first type of monitoring – data drift monitoring. For the other three types of monitoring, we will only briefly mention them.

Data drift monitoring

You monitor a production model for data drift to ensure that the distribution of the live inference traffic the deployed model is serving does not drift away from the distribution of the dataset used for training the model. The end-to-end architecture...