10-minute tutorials: Get started with machine learning on Databricks

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.

Note

The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 9.1 LTS or above.

scikit-learn notebooks

Notebook

Requirements

Features

Machine learning quickstart

Databricks Runtime ML

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

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