Tutorial: End-to-end deep learning models on Azure Databricks

This tutorial notebook presents an end-to-end example of training a deep learning model in Azure Databricks, including loading data, visualizing the data, setting up a parallel hyperparameter optimization, and using MLflow to review the results, register the model, and perform inference on new data using the registered model in a Spark UDF.

The notebook uses PyTorch, a Python package that provides GPU-accelerated tensor computation and high level functionality for building deep learning networks.

When you're ready you can deploy your model using Deploy models using Mosaic AI Model Serving.

MLflow PyTorch model training notebook

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