What it does
This is Amazon Web Services' official collection of Jupyter notebooks (documents that mix explanations with runnable code) for SageMaker, its cloud service for machine learning.
The examples follow a project from start to finish: training a model, customising an existing model with your own data, testing whether it got better, putting it online for others to use, and running it reliably over time.
Who it's for
Data scientists and engineers who use, or plan to use, Amazon's cloud for machine learning.
Can I use it without coding?
Developers only. You need an AWS account, cloud setup and some Python to run the notebooks. New to this? Start here.
Can I run it?
- Runs on
- Cloud
- Privacy
- Sends what you give it to an online service
- API key
- Not needed
Try it
- Set up an AWS account with SageMaker access.
- Open a SageMaker notebook, where these examples are already loaded under the SageMaker Examples tab.
Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?
Before you install
- Only download from the official GitHub page. Copies with similar names exist for many popular projects.
- License (Apache-2.0): Permissive: free to use, change and share, including for business.
- What you type or upload is sent to an online service. Avoid sharing private or sensitive information.
- More on staying safe