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

In this quickstart, you 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 notebook server instead, see Quickstart: Use Python to get started with Azure Machine Learning.

In this quickstart, you:

  • Create a workspace in your Azure subscription.
  • Try it out with Python in an Azure 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 Azure Batch AI cluster size. For information on default limits and how to increase your quota, see this article.

If you don’t have an Azure subscription, create a free account before you begin.

Create a workspace

Sign in to the Azure portal by using the credentials for the Azure subscription you use. If you don't have an Azure subscription, create a free account now.

The portal's workspace dashboard is supported on Edge, Chrome, and Firefox browsers only.

Azure portal

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

Create a resource in Azure portal

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

Search for workspace

In the Machine Learning service workspace pane, scroll to the bottom and select Create to begin.

Create

In the ML service workspace pane, configure your workspace.

Field Description
Workspace name Enter a unique name that identifies your workspace. Here 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. Here 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

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

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

Workspace creation status

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.

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

Explore workspace

Use the workspace

Now see how a workspace helps you manage your machine learning scripts. In this section, you:

  • 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 are preconfigured with everything you need to run Machine Learning.

Select Open Azure Notebooks to try your first experiment.

Open Azure Notebooks

Your organization might require administrator consent before you can sign in.

After you sign in, a new tab opens and a Clone Library prompt appears. Select Clone

Run the notebook

Along with two notebooks, you see a config.json file. This config file contains information about the workspace you created.

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

To run the cells one at a time, use Shift+Enter. Or select Cells > Run All to run the entire notebook. When you see an asterisk [*] next to a cell, it's running. After the code for that cell finishes, a number appears.

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

View logged values

After you run all the cells in the notebook, go back to the portal page.

Select View Experiments.

View experiments

Close the Reports pop-up.

Select my-first-experiment.

See information about the run you just performed. Scroll down the page to find the table of runs. Select the run number link.

Run history link

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

View history

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

Clean up resources

Important

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 here, delete them so you don't incur any charges.

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

    Delete in Azure portal

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

  3. Select Delete resource group.

  4. Enter the resource group name, and then select Delete.

    If you see the error message "Cannot delete resource before nested resources are deleted," you must delete any nested resources first. For information on how to delete nested resources, see this troubleshooting section.

You also can 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.