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 |
---|---|---|
Databricks Runtime 7.5 ML or above |
Classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow |
|
Databricks Runtime ML |
Classification model, MLflow, automated hyperparameter tuning with Hyperopt and MLflow, Model Registry |
|
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 |
---|---|---|
Databricks Runtime ML |
Logistic regression model, Spark pipeline, automated hyperparameter tuning using MLlib API |