Tutorial: End-to-end classic ML models on Azure Databricks

This tutorial notebook presents an end-to-end example of training a classic ML model in Azure Databricks, including loading data, visualizing the data, setting up a parallel hyperparameter optimization, and using MLflow to review the results, register the model, and perform inference on new data using the registered model in a Spark UDF.

You can import this notebook and run it yourself, or copy code-snippets and ideas for your own use.

Notebook

MLflow 3

This version of the notebook uses MLflow 3 and Unity Catalog.

XGBoost MLflow 3 tutorial (Unity Catalog)

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MLflow 2.x

If your workspace is enabled for Unity Catalog, use this version of the notebook:

XGBoost MLflow tutorial (Unity Catalog)

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If your workspace is not enabled for Unity Catalog, use this version of the notebook:

Use scikit-learn with MLflow integration on Databricks

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