Welcome to Amazon SageMaker. This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.
This site is based on the SageMaker Examples repository on GitHub. Browse around to see what piques your interest. To run these notebooks, you will need a SageMaker Notebook Instance or SageMaker Studio. Refer to the SageMaker developer guide's Get Started page to get one of these set up.
On a Notebook Instance, the examples are pre-installed and available from the examples menu item in JupyterLab. On SageMaker Studio, you will need to open a terminal, go to your home folder, then clone the repo with the following:
git clone https://github.com/aws/amazon-sagemaker-examples.git
.. toctree:: :maxdepth: 1 :hidden: :caption: Get started get_started/index
.. toctree:: :maxdepth: 1 :caption: Featured examples aws_sagemaker_studio/index
.. toctree:: :maxdepth: 1 :caption: Autopilot autopilot/index
.. toctree:: :maxdepth: 1 :caption: Preprocessing preprocessing/index ground_truth_labeling_jobs/index
.. toctree:: :maxdepth: 1 :caption: Training training/index
.. toctree:: :maxdepth: 1 :caption: Inference inference/index
.. toctree:: :maxdepth: 1 :caption: Frameworks training/frameworks
.. toctree:: :maxdepth: 1 :caption: Workflows sagemaker_processing/index sagemaker-spark/index step-functions-data-science-sdk/index
.. toctree:: :maxdepth: 1 :caption: Advanced examples scientific_details_of_algorithms/index aws_marketplace/index
.. toctree:: :maxdepth: 1 :caption: Community examples contrib/index