Amazon SageMaker Studio — Intermediate Playground

AWS cloud IDE for ML: notebooks, experiment tracking, model deployment

Using Amazon SageMaker Studio effectivelyRun locally

Install

Via AWS Console → SageMaker

Python CodeRun locally

# Amazon SageMaker Studio - effective usage
# AWS cloud IDE for ML: notebooks, experiment tracking, model deployment
# Best practices and workflows
print("Amazon SageMaker Studio best practices")

# Amazon SageMaker Studio - effective usage
# AWS cloud IDE for ML: notebooks, experiment tracking, model deployment
# Best practices and workflows
print("Amazon SageMaker Studio best practices")

Following best practices with Amazon SageMaker Studio ensures a productive development workflow.

Challenge

Try modifying the code above to explore different behaviors. Can you extend the example to handle a new use case?

View all Intermediate examples for Amazon SageMaker Studio