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, we tackled feature engineering for a large (~ 500 GB) dataset. We looked at challenges including scalability, bias, and explainability. We saw how to use SageMaker Data Wrangler, Clarify, and Processing jobs to explore and prepare data.

While there are many ways to use these tools, we recommend using Data Wrangler for interactive exploration of small to mid-sized datasets. For processing large datasets in their entirety, switch to programmatic use of processing jobs using the Spark framework to take advantage of parallel processing. (At the time of writing, Data Wrangler does not support running on multiple instances, but you can run a processing job on multiple instances.) You can always export a Data Wrangler flow as a starting point.

If your dataset is many terabytes, consider running a Spark job directly in EMR or Glue and invoking SageMaker using the SageMaker Spark SDK. EMR and Glue have optimized Spark runtimes and more efficient integration with...