Tutorial: End-to-end ML models on Azure Databricks
This tutorial notebook presents an end-to-end example of training a 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
If your workspace is enabled for Unity Catalog, use this version of the notebook:
Use scikit-learn with MLflow integration on Databricks (Unity Catalog)
If your workspace is not enabled for Unity Catalog, use this version of the notebook: