Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text.
Starting today, SageMaker provides four new built-in tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer. You can use these popular, state-of-the-art algorithms for both tabular classification and regression tasks. They’re available through the built-in algorithms on the SageMaker console as well as through the Amazon SageMaker JumpStart UI inside Amazon SageMaker Studio.
The following is the list of the four new built-in algorithms, with links to their documentation, example notebooks, and source.

Example Notebooks

LightGBM Algorithm
Regression, Classification

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