End-to-end example using scikit-learn on Databricks


The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 9.1 LTS or above.

This notebook uses scikit-learn to illustrate a complete end-to-end example of loading data, model training, distributed hyperparameter tuning, and model inference. It also illustrates how to use MLflow and Model Registry for logging and registering your model. You can import this notebook and run it in your Databricks workspace.


Databricks Runtime ML


The following notebook may include functionality that is not available in this release of Databricks on Google Cloud.

Use scikit-learn with MLflow integration notebook

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