Tutorials: Get started with ML
The notebooks in this article are designed to get you started quickly with machine learning on Azure Databricks. You can import each notebook to your Azure Databricks workspace to run them.
These notebooks illustrate how to use Azure Databricks throughout the machine learning lifecycle, including data loading and preparation; model training, tuning, and inference; and model deployment and management. They also demonstrate helpful tools such as Hyperopt for automated hyperparameter tuning, MLflow tracking and autologging for model development, and Model Registry for model management.
scikit-learn notebooks
Notebook | Requirements | Features |
---|---|---|
Machine learning tutorial | Databricks Runtime ML | Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow |
End-to-end example | Databricks Runtime ML | Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, XGBoost |
Apache Spark MLlib notebook
Notebook | Requirements | Features |
---|---|---|
Machine learning with MLlib | Databricks Runtime ML | Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API |
Deep learning notebook
Notebook | Requirements | Features |
---|---|---|
Deep learning with TensorFlow Keras | Databricks Runtime ML | Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry |
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