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 data science environment

In the previous section, we introduced high-level Amazon SageMaker features that can often be used in isolation or together for end-to-end capabilities. In this section, we will focus on creating consistent and repeatable governed data science environments that can take advantage of the features discussed in the first section.

To build, train, and deploy models using Amazon SageMaker, ML builders need access to select AWS resources spanning the ML development life cycle. Because many different personas may be responsible for building ML models, the term ML builder refers to any individual tasked with model building. This could include data scientists, ML engineers, or data analysts.

Data science development environments provide ML builders with the AWS resources they need to build and train models. A data science environment could be as simple as an AWS account with access to Amazon SageMaker as well as AWS services commonly used with Amazon...