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

Basic concepts of Amazon SageMaker Model Monitor and Amazon SageMaker Clarify

In this section, let's review the capabilities provided by two SageMaker features: Model Monitor and Clarify.

Amazon SageMaker Model Monitor provides capabilities to monitor data drift and the model quality of models deployed as SageMaker real-time endpoints. Amazon SageMaker Clarify provides capabilities to monitor the deployed model for bias and feature attribution drift. Using a combination of these two features, you can monitor the following four different aspects of ML models deployed on SageMaker:

  • Data drift: If the live inference traffic data served by the deployed model is statistically different from the training data the model was trained on, the model prediction accuracy will start to deteriorate. Using a combination of a training data baseline and periodic monitoring to compare the incoming inference requests with the baseline data, SageMaker Model Monitor detects data drift...