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AutoGluon
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`AutoGluon-Tabular <https://auto.gluon.ai/stable/index.html>`__ is a popular open-source AutoML framework that trains highly accurate machine learning models on an unprocessed tabular dataset.
Unlike existing AutoML frameworks that primarily focus on model and hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers.


The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker AutoGluon-Tabular algorithm.

.. list-table::
   :widths: 25 25
   :header-rows: 1

   * - Notebook Title
     - Description
   * - `Tabular classification with Amazon SageMaker AutoGluon-Tabular algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/autogluon_tabular/Amazon_Tabular_Classification_AutoGluon.ipynb>`__
     - This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular classification model.
   * - `Tabular regression with Amazon SageMaker AutoGluon-Tabular algorithm <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/autogluon_tabular/Amazon_Tabular_Regression_AutoGluon.ipynb>`__
     - This notebook demonstrates the use of the Amazon SageMaker AutoGluon-Tabular algorithm to train and host a tabular regression model.


For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see
`Use Amazon SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html>`__. After you have created a notebook
instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its
Use tab and choose Create copy.

For detailed documentation, please refer to the `Sagemaker AutoGluon-Tabular Algorithm <https://docs.aws.amazon.com/sagemaker/latest/dg/autogluon-tabular.html>`__.