Quickstart: Create workspace resources you need to get started with Azure Machine Learning

In this quickstart, you'll create a workspace and then add compute resources to the workspace. You'll then have everything you need to get started with Azure Machine Learning.

The workspace is the top-level resource for your machine learning activities, providing a centralized place to view and manage the artifacts you create when you use Azure Machine Learning. The compute resources provide a pre-configured cloud-based environment you can use to train, deploy, automate, manage, and track machine learning models.


Create the workspace

If you already have a workspace, skip this section and continue to Create a compute instance.

If you don't yet have a workspace, create one now:

  1. Sign in to the Azure portal by using the credentials for your Azure subscription.

  2. In the upper-left corner of the Azure portal, select the three bars, then + Create a resource.

    Screenshot showing + Create a resource.

  3. Use the search bar to find Azure Machine Learning.

  4. Select Azure Machine Learning.

    Screenshot shows search results to select Machine Learning.

  5. In the Machine Learning pane, select Create to begin.

  6. 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.
    Region Select the location closest to your users and the data resources to create your workspace.
    Storage account A storage account is used as the default datastore for the workspace. You may create a new Azure Storage resource or select an existing one in your subscription.
    Key vault A key vault is used to store secrets and other sensitive information that is needed by the workspace. You may create a new Azure Key Vault resource or select an existing one in your subscription.
    Application insights The workspace uses Azure Application Insights to store monitoring information about your deployed models. You may create a new Azure Application Insights resource or select an existing one in your subscription.
    Container registry A container registry is used to register docker images used in training and deployments. You may choose to create a resource or select an existing one in your subscription.
  7. After you're finished configuring the workspace, select Review + Create.

  8. Select Create to create the workspace.


    It can take several minutes to create your workspace in the cloud.

    When the process is finished, a deployment success message appears.

  9. To view the new workspace, select Go to resource.

  10. From the portal view of your workspace, select Launch studio to go to the Azure Machine Learning studio.

Create compute instance

You could install Azure Machine Learning on your own computer. But in this quickstart, you'll create an online compute resource that has a development environment already installed and ready to go. You'll use this online machine, a compute instance, for your development environment to write and run code in Python scripts and Jupyter notebooks.

Create a compute instance to use this development environment for the rest of the tutorials and quickstarts.

  1. If you didn't select Go to workspace in the previous section, sign in to Azure Machine Learning studio now, and select your workspace.
  2. On the left side, select Compute.
  3. Select +New to create a new compute instance.
  4. Supply a name, Keep all the defaults on the first page.
  5. Select Create.

In about two minutes, you'll see the State of the compute instance change from Creating to Running. It's now ready to go.

Create compute clusters

Next you'll create a compute cluster. Clusters allow you to distribute a training or batch inference process across a cluster of CPU or GPU compute nodes in the cloud.

Create a compute cluster that will autoscale between zero and four nodes:

  1. Still in the Compute section, in the top tab, select Compute clusters.
  2. Select +New to create a new compute cluster.
  3. Keep all the defaults on the first page, select Next. If you don't see any available compute, you'll need to request a quota increase. Learn more about managing and increasing quotas.
  4. Name the cluster cpu-cluster. If this name already exists, add your initials to the name to make it unique.
  5. Leave the Minimum number of nodes at 0.
  6. Change the Maximum number of nodes to 4 if possible. Depending on your settings, you may have a smaller limit.
  7. Change the Idle seconds before scale down to 2400.
  8. Leave the rest of the defaults, and select Create.

In less than a minute, the State of the cluster will change from Creating to Succeeded. The list shows the provisioned compute cluster, along with the number of idle nodes, busy nodes, and unprovisioned nodes. Since you haven't used the cluster yet, all the nodes are currently unprovisioned.


When the cluster is created, it will have 0 nodes provisioned. The cluster does not incur costs until you submit a job. This cluster will scale down when it has been idle for 2,400 seconds (40 minutes). This will give you time to use it in a few tutorials if you wish without waiting for it to scale back up.

Quick tour of the studio

The studio is your web portal for Azure Machine Learning. This portal combines no-code and code-first experiences for an inclusive data science platform.

Review the parts of the studio on the left-hand navigation bar:

  • The Author section of the studio contains multiple ways to get started in creating machine learning models. You can:

    • Notebooks section allows you to create Jupyter Notebooks, copy sample notebooks, and run notebooks and Python scripts.
    • Automated ML steps you through creating a machine learning model without writing code.
    • Designer gives you a drag-and-drop way to build models using prebuilt components.
  • The Assets section of the studio helps you keep track of the assets you create as you run your jobs. If you have a new workspace, there's nothing in any of these sections yet.

  • You already used the Manage section of the studio to create your compute resources. This section also lets you create and manage data and external services you link to your workspace.

Workspace diagnostics

You can run diagnostics on your workspace from Azure Machine Learning studio or the Python SDK. After diagnostics run, a list of any detected problems is returned. This list includes links to possible solutions. For more information, see How to use workspace diagnostics.

Clean up resources

If you plan to continue now to the next tutorial, skip to Next steps.

Stop compute instance

If you're not going to use it now, stop the compute instance:

  1. In the studio, on the left, select Compute.
  2. In the top tabs, select Compute instances
  3. Select the compute instance in the list.
  4. On the top toolbar, select Stop.

Delete all resources


The resources that you created can be used as prerequisites to other Azure Machine Learning tutorials and how-to articles.

If you don't plan to use any of the resources that you created, delete them so you don't incur any charges:

  1. In the Azure portal, select Resource groups on the far left.

  2. From the list, select the resource group that you created.

  3. Select Delete resource group.

    Screenshot of the selections to delete a resource group in the Azure portal.

  4. Enter the resource group name. Then select Delete.

Next steps

You now have an Azure Machine Learning workspace that contains:

  • A compute instance to use for your development environment.
  • A compute cluster to use for submitting training runs.

Use these resources to learn more about Azure Machine Learning and train a model with Python scripts.