Tutorials: Get started with AI and machine learning

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The notebooks in this section are designed to get you started quickly with AI and machine learning on Mosaic AI. You can import each notebook to your Azure Databricks workspace to run them.

These notebooks illustrate how to use Azure Databricks throughout the AI lifecycle, including data loading and preparation; model training, tuning, and inference; and model deployment and management.

Classical ML tutorials

Notebook Requirements Features
End-to-end example Databricks Runtime ML Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, XGBoost
Machine learning with scikit-learn Databricks Runtime ML Unity Catalog, classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
Machine learning with MLlib Databricks Runtime ML Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API
Deep learning with TensorFlow Keras Databricks Runtime ML Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry