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

Creating CI/CD pipelines using Amazon SageMaker Projects

In this section, we'll discuss using Amazon SageMaker Projects to incorporate CI/CD practices into your ML pipelines. SageMaker Projects is a service that uses SageMaker Pipelines and the SageMaker model registry, in combination with CI/CD tools, to automatically provision and configure CI/CD pipelines for ML. Figure 12.10 illustrates 
the core components of SageMaker Projects. With Projects, you have the advantage of a CD pipeline, source code versioning, and automatic triggers for pipeline execution:

Figure 12.10 – SageMaker Projects

Projects are made available through built-in SageMaker MLOps project templates or by creating your own organization's MLOps templates. The underlying templates are offered through AWS Service Catalog, via SageMaker Studio, and contain CloudFormation templates that preconfigure CI/CD pipelines for the selected template. Because projects rely on...