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)
1
Section 1: Processing Data at Scale
7
Section 2: Model Training Challenges
10
Section 3: Manage and Monitor Models
15
Section 4: Automate and Operationalize Machine Learning

Best practices for monitoring ML models

This section discusses best practices for monitoring models using SageMaker Model Monitor and SageMaker Clarify, taking into consideration the under-the-hood operation of these features and a few limitations as they stand at the time of publication of this book:

  • Choosing the correct data format: Model Monitor and Clarify can only monitor for drift in tabular data. Therefore, ensure that your training data is in tabular format. For other data formats, you will have to build custom monitoring containers.
  • Choosing real-time endpoints as the mode of model deployment: Model Monitor and Clarify support monitoring for a single-model real-time endpoint. Monitoring a model used with batch transform or multi-model endpoints is not supported. So, ensure that the model you want to monitor is deployed as a single-model real-time endpoint. Additionally, if the model is part of an inference pipeline, the entire pipeline is monitored, not the individual...