The Azure Databricks model registry is a powerful tool for model registration, model versioning, and tagging models for deployment. In this exercise, you will learn how to use the model registry, through both the User Interface as well as the MLflow API. To begin, you need to have access to an Azure Databricks workspace with an interactive cluster. If you do not have a workspace and/or the required cluster, follow the instructions below. Otherwise, you can skip to the section Upload the Databricks notebook archive.
Before starting this lab, complete the Getting Started with Azure Databricks lab to set up your Azure Databricks environment and import the data and notebooks you require.
In this exercise, you will learn how to manage models.
In a web browser, open your Azure Databricks workspace.
If your cluster is not running, on the Compute page, select your cluster and use the ▶ Start button to start it
In the Azure Databricks Workspace, using the command bar on the left, select Workspace. Then select Users, and ☗ your_user_name. Then in the folder named 03 - Managing Experiments and Models, open the 02 - Managing Models notebook.
Attach the notebook to your cluster. Then read the notes in the notebook, running each code cell in turn.
Tip: For the first section, Managing a Model via the User Interface, there will be instructions on what actions to perform, as many of these actions will take place outside of the confines of a notebook. It may be easiest to open up a second tab in your browser and perform these actions in that tab while reviewing the instructions in the notebook.
If you’re finished working with Azure Databricks for now, in Azure Databricks workspace, on the Compute page, select your cluster and select ■ Terminate to shut it down. Otherwise, leave it running for the next exercise.