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 quickstart Databricks Runtime 7.5 ML or above Classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow
Machine learning with Model Registry Databricks Runtime ML Classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, Model Registry
End-to-end example Databricks Runtime ML Classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, XGBoost, Model Registry, Model Serving

Apache Spark MLlib notebook

Notebook Requirements Features
Machine learning with MLlib Databricks Runtime 7.3 LTS ML or above Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API

Deep learning notebook

Notebook Requirements Features
Deep learning with TensorFlow Keras Databricks Runtime 7.3 ML or above Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry