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.
Neural network model, inline TensorBoard, automated hyperparameter tuning with Hyperopt and MLflow, autologging, ModelRegistry
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