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


In this chapter, you learned the importance of monitoring ML models deployed in production and the different aspects of models to monitor. You dove deep into multiple end-to-end architectures to build continuous monitoring, automate responses to detected data, and model issues using SageMaker Model Monitor and SageMaker Clarify. You learned how to use the various metrics and reports generated to gain insight into your data and model.

Finally, we concluded with a discussion on the best practices for configuring model monitoring. Using the concepts discussed in this chapter, you can build a comprehensive monitoring solution to meet your performance and regulatory requirements, without having to use various different third-party tools for monitoring various aspects of your model.

In the next chapter, we will introduce end-to-end ML workflows that stitch all the individual steps involved in the ML process together.