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.
|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
|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
|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|
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