Train a scikit-learn model and save in scikit-learn format


The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 8.1 or above.

This notebook is based on the MLflow tutorial.

The notebook shows how to use MLflow to track the model training process, including logging model parameters, metrics, the model itself, and other artifacts like plots to a Databricks hosted tracking server. It also includes instructions for viewing the logged results in the MLflow tracking UI.

MLflow scikit-learn model training notebook

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