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

Building ML workflows with Amazon SageMaker Pipelines

Model build workflows cover all of the steps performed when developing your model, including data preparation, model training, model tuning, and model deployment. In this case, model deployment can include the tasks necessary to evaluate your model, as well as batch use cases that do not need to be deployed to higher environments. SageMaker Pipelines is a fully managed service that allows you to create automated model build workflows using the SageMaker Python SDK.

SageMaker Pipelines includes built-in step types ( for executing SageMaker tasks, such as SageMaker Processing for data pre-processing, and SageMaker Training for model training. Pipelines also include steps for controlling how your pipeline works. For example, the pipeline could include conditional steps that could be used to evaluate the output of a previous step to determine whether to...