Amazon SageMaker Autopilot has added a new training mode that supports model ensembling powered by AutoGluon. Ensemble training mode in Autopilot trains several base models and combines their predictions using model stacking. For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than hyperparameter optimization (HPO) training mode with 250 trials, and up to 5.8 times faster than HPO training mode with 100 trials. It supports a wide range of algorithms, including LightGBM, CatBoost, XGBoost, Random Forest, Extra Trees, linear models, and neural networks based on PyTorch and FastAI.
How AutoGluon builds ensemble models
AutoGluon-Tabular (AGT) is a popular open-source AutoML framework that trains highly accurate ML models on tabular datasets. Unlike existing AutoML frameworks, which primarily focus on model and hyperparameter selection, AGT succeeds by ensembling multiple models and stacking them in multiple layers. The default behavior

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