Create and manage Azure Machine Learning service workspaces
In this article, you'll create, view, and delete Azure Machine Learning service workspaces in the Azure portal for Azure Machine Learning service. The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces using the CLI, with Python code or via the VS Code extension.
Create a workspace
To create a workspace, you need an Azure subscription. If you don’t have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning service today.
Sign in to the Azure portal by using the credentials for the Azure subscription you use.
In the upper-left corner of Azure portal, select + Create a resource.
Use the search bar to find Machine Learning service workspace.
Select Machine Learning service workspace.
In the Machine Learning service workspace pane, select Create to begin.
Configure your new workspace by providing the workspace name, subscription, resource group, and location.
Field Description Workspace name Enter a unique name that identifies your workspace. In this example, we use docs-ws. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others. Subscription Select the Azure subscription that you want to use. Resource group Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use docs-aml. Location Select the location closest to your users and the data resources to create your workspace.
After you are finished configuring the workspace, select Create.
It can take a few moments to create the workspace.
When the process is finished, a deployment success message appears. To view the new workspace, select Go to resource.
Download a configuration file
If you will be creating a Notebook VM, skip this step.
If you plan to use code on your local environment that references this workspace, select Download config.json from the Overview section of the workspace.
Place the file into the directory structure with your Python scripts or Jupyter Notebooks. It can be in the same directory, a subdirectory named .azureml, or in a parent directory. When you create a Notebook VM, this file is added to the correct directory on the VM for you.
View a workspace
In top left corner of the portal, select All services.
In the All services filter field, type machine learning service.
Select Machine Learning service workspaces.
Look through the list of workspaces found. You can filter based on subscription, resource groups, and locations.
Select a workspace to display its properties.
Delete a workspace
Use the Delete button at the top of the workspace you wish to delete.
Clean up resources
The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles.
If you don't plan to use the resources you created, delete them, so you don't incur any charges:
In the Azure portal, select Resource groups on the far left.
From the list, select the resource group you created.
Select Delete resource group.
Enter the resource group name. Then select Delete.
Follow the full-length tutorial to learn how to use a workspace to build, train, and deploy models with Azure Machine Learning service.