AI and machine learning tutorials

  • 06/17/2025

The tutorials in this section illustrate how to use Azure Databricks throughout the AI lifecycle for classical ML and gen AI workloads.

Classical ML tutorials

You can import each notebook to your Azure Databricks workspace to run them.

Notebook Features
Machine learning with scikit-learn Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Optuna and MLflow
Machine learning with MLlib Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API
Deep learning with TensorFlow Keras Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry

Deep learning tutorial

Notebook Requirements Features
End-to-end PyTorch example Databricks Runtime ML Unity Catalog, PyTorch, MLflow, automated hyperparameter tuning with Optuna and MLflow