Deploying Models in Azure Machine Learning

Machine Learning is primarily about training models that you can use to provide predictive services to applications. In this exercise, you will learn to train models in Azure Databricks and then deploy models in Azure Machine Learning.

Prerequisites

Before starting this lab, complete the Getting Started with Azure Databricks and Running experiments in Azure Machine Learning lab to set up Azure Databricks and Azure machine Learning environments.

Install libraries on the Azure Databricks Cluster

The notebooks you will run depends on certain Python libraries that will need to be installed in your cluster. The following steps walk you through adding these dependencies.

  • From within the Azure Databricks workspace, from the Clusters section, select your cluster. Make sure the state of the cluster is Running.
  • Select the Libraries link and then select Install New.
  • In the Library Source, select PyPi and in the Package text box type azureml-sdk[databricks] and select Install.
  • Next install sklearn-pandas==2.1.0
  • Next install azureml-mlflow

Deploy a Model in Azure Machine Learning

In this exercise, you will learn how to load and manipulate data inside the Azure Databricks environment.

  1. In a web browser, open your Azure Databricks workspace.

  2. If your cluster is not running, on the Compute page, select your cluster and use the ▶ Start button to start it

  3. 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 04 - Integrating Azure Databricks and Azure Machine Learning, open the 2.0 Deploying Models in Azure Machine Learning notebook.

  4. Attach the notebook to your cluster. Then read the notes in the notebook, running each code cell in turn.

Clean-up

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.

If you have finished exploring Azure Databricks, you can delete the resource groups in your Azure subscription that contain the Azure Databricks and Azure Machine Learning resources.