Tutorials: Get started with ML

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

These notebooks illustrate how to use 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 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