The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 8.1 or above.
The notebooks in this section are designed to get you started quickly with machine learning on Databricks. They 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 demonstrate helpful tools such as Hyperopt for automated hyperparameter tuning, MLflow tracking and autologging for model development, and Model Registry for model management.
|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|
|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|