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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.
If your workspace is enabled for Unity Catalog, use this version of the notebook:
If your workspace is not enabled for Unity Catalog, use this version of the notebook:
Events
Mar 31, 11 PM - Apr 2, 11 PM
The ultimate Microsoft Fabric, Power BI, SQL, and AI community-led event. March 31 to April 2, 2025.
Register todayTraining
Module
Use MLflow in Azure Databricks - Training
Learn how to use MLflow in Azure Databricks to track machine learning experiments and deploy models.
Certification
Microsoft Certified: Azure Data Scientist Associate - Certifications
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.