Classic machine learning

Important

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Azure Databricks previews.

This page provides notebook examples for classic machine learning tasks using Serverless GPU compute. These examples demonstrate how to leverage GPUs for traditional ML algorithms and time series forecasting.

XGBoost model training

This notebook demonstrates how to train an XGBoost regression model on a single GPU. XGBoost can significantly benefit from GPU acceleration for large datasets.

XGBoost

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Distributed XGBoost Hyperparameter Tuning using Ray

This notebook demonstrates end-to-end distributed XGBoost training with hyperparameter optimization using Ray Tune on Databricks Serverless GPU Compute.

RayTuneXGBoost

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Time series forecasting with GluonTS

This notebook demonstrates an end-to-end workflow for probabilistic time-series forecasting of electricity consumption data with GluonTS's DeepAR model on a serverless GPU cluster. It covers data ingestion, resampling, model training, prediction, visualization, and evaluation.

GluonTS

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