Quickstart: Use the Azure portal to get started with Azure Machine Learning

Use the Azure portal to create an Azure Machine Learning workspace. This workspace is the foundational block in the cloud that you use to experiment, train, and deploy machine learning models with Machine Learning. This quickstart uses cloud resources and requires no installation. To configure your own Jupyter Notebooks server instead, see Quickstart: Use Python to get started with Azure Machine Learning.

In this quickstart, you take the following actions:

  • Create a workspace in your Azure subscription.
  • Try it out with Python in a Jupyter notebook and log values across multiple iterations.
  • View the logged values in your workspace.

The following Azure resources are added automatically to your workspace when they're regionally available:

The resources you create can be used as prerequisites to other Machine Learning service tutorials and how-to articles. As with other Azure services, there are limits on certain resources associated with Machine Learning. An example is compute cluster size. Learn more about the default limits and how to increase your quota.

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.

Create a workspace

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

    Azure portal

  2. In the upper-left corner of the portal, select Create a resource.

    Create a resource in Azure portal

  3. In the search bar, enter Machine Learning. Select the Machine Learning service workspace search result.

    Search for a workspace

  4. In the ML service workspace pane, scroll to the bottom and select Create to begin.


  5. In the ML service workspace pane, configure your 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 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 is a container that 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. This location is where the workspace is created.

    Create workspace

  6. To start the creation process, select Create. It can take a few moments to create the workspace.

  7. To check on the status of the deployment, select the Notifications icon, bell, on the toolbar.

  8. When the process is finished, a deployment success message appears. It's also present in the notifications section. To view the new workspace, select Go to resource.

    Workspace creation status

Use the workspace

Now learn how a workspace helps you manage your machine learning scripts. In this section, you take the following steps:

  • Open a notebook in Azure Notebooks.
  • Run code that creates some logged values.
  • View the logged values in your workspace.

This example shows how the workspace can help you keep track of information generated in a script.

Open a notebook

Azure Notebooks provides a free cloud platform for Jupyter notebooks that is preconfigured with everything you need to run Machine Learning. From your workspace you can launch this platform to get started using your Azure Machine Learning service workspace.

  1. On the workspace page, select Explore your Azure Machine Learning service Workspace.

    Explore the workspace

  2. Select Open Azure Notebooks to try your first experiment in Azure Notebooks. Azure Notebooks is a separate service that lets you run Jupyter notebooks for free in the cloud. When you use this link to the service, information about how to connect to your workspace will be added to the library you create in Azure Notebooks.

    Open Azure Notebooks

  3. Sign into Azure Notebooks. Make sure you sign in with the same account you used to sign into the Azure portal. Your organization might require administrator consent before you can sign in.

  4. After you sign in, a new tab opens and a Clone Library prompt appears. Cloning this library will load a set of notebooks and other files into your Azure Notebooks account. These files help you explore the capabilities of Azure Machine Learning.

  5. Uncheck Public so that you don't share your workspace information with others.

  6. Select Clone.

    Clone a library

  7. If you see that the project status is stopped, click on Run on Free Compute to use the free notebook server.

    Run a project on free compute

Run the notebook

In the list of files for this project, you see a config.json file. This config file contains information about the workspace you created in the Azure portal. This file allows your code to connect to and add information into your workspace.

  1. Select 01.run-experiment.ipynb to open the notebook.

  2. The status area tells you to wait until the kernel has started. The message disappears once the kernel is ready.

    Wait for kernel to start

  3. After the kernel has started, run the cells one at a time using Shift+Enter. Or select Cells > Run All to run the entire notebook. When you see an asterisk, *, next to a cell, the cell is still running. After the code for that cell finishes, a number appears.

  4. Follow instructions in the notebook to authenticate your Azure subscription.

After you've finished running all of the cells in the notebook, you can view the logged values in your workspace.

View logged values

  1. The output from the run cell contains a link back to the Azure portal to view the experiment results in your workspace.

    View experiments

  2. Click the Link to Azure Portal to view information about the run in your workspace. This link opens your workspace in the Azure portal.

  3. The plots of logged values you see were automatically created in the workspace. Whenever you log multiple values with the same name parameter, a plot is automatically generated for you.

    View history

Because the code to approximate pi uses random values, your plots will show different values.

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:

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

    Delete in the Azure portal

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

  3. Select Delete resource group.

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

You can also keep the resource group but delete a single workspace. Display the workspace properties and select Delete.

Next steps

You created the necessary resources to experiment with and deploy models. You also ran some code in a notebook. And you explored the run history from that code in your workspace in the cloud.

For an in-depth workflow experience, follow Machine Learning tutorials to train and deploy a model: