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Understanding Amazon SageMaker Machine Learning Environments

Difficulty: Beginner
Duration: 2 minutes and 48 seconds
Students: 37
Rating: 5/5

This lesson will cover the different machine learning environments available in Amazon SageMaker, including who should use them, why they are useful, and what problems they solve.

Learning Objectives

By the end of this lesson, you will have a greater understanding of Amazon SageMaker’s machine learning environments, including: 

  • The functional differences between SageMaker Studio, SageMaker Studio Classic, and SageMaker StudioLab, 

  • The Integrated Development Environment or IDE options in Amazon SageMaker Studio and the use cases for each, including:  

  • SageMaker JupyterLab,  

  • SageMaker RStudio, 

  • SageMaker Code Editor,  

  • And SageMaker Notebook Instances  

  • The purpose-built ML environments available in SageMaker, such as SageMaker Canvas, SageMaker geospatial capabilities, and the SageMaker HyperPod environments 

Intended Audience:

  • Those who are getting started with Amazon SageMaker and need more information on the different environments it offers. This may include users in roles such as machine learning engineers, data scientists, and software engineers.  

Prerequisites: 

  • Have a fundamental understanding of AWS and its global infrastructure

  • Have basic knowledge of the Amazon SageMaker service

  • Be familiar with Amazon EC2, including EC2 instance types and Amazon Machine Images, or AMIs.

  • For more information on these services please see our existing content titled: