Create and manage Azure Machine Learning workspaces in the Azure portal
APPLIES TO: Basic edition Enterprise edition (Upgrade to Enterprise edition)
In this article, you'll create, view, and delete Azure Machine Learning workspaces in the Azure portal for Azure Machine Learning. 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 today.
Sign in to the Azure portal by using the credentials for your Azure subscription.
In the upper-left corner of Azure portal, select + Create a resource.
Use the search bar to find Machine Learning.
Select Machine Learning.
In the Machine Learning pane, select Create to begin.
Provide the following information to configure your new workspace:
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. Workspace edition Select Basic or Enterprise. This workspace edition determines the features to which you’ll have access and pricing. Learn more about Basic and Enterprise edition offerings.
After you are finished configuring the workspace, select Create.
It can take several minutes to create your workspace in the cloud.
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.
Upgrade to Enterprise edition
You can upgrade your workspace from Basic edition to Enterprise edition to take advantage of the enhanced features such low-code experiences and enhanced security features.
Sign in to Azure Machine Learning studio.
Select the workspace that you wish to upgrade.
Select Learn more at the top right of the page.
Select Upgrade in the window that appears.
You cannot downgrade an Enterprise edition workspace to a Basic edition workspace.
Find a workspace
In the top search field, type Machine Learning.
Select Machine Learning.
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 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.