Tutorial: Create resources you need to get started

In this tutorial, you create the resources you need to start working with Azure Machine Learning.

In this tutorial, you create your resources in Azure Machine Learning studio.

You can also create a workspace using the Azure portal or SDK, the CLI, Azure PowerShell, or the Visual Studio Code extension.

For other ways to create a compute instance, see Create a compute instance.

This video shows you how to create a workspace and compute instance in Azure Machine Learning studio. The steps are also described in the sections below.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

Create the workspace

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.

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 Azure Machine Learning studio.

2. Select Create workspace.

3. Provide the following information to configure your new workspace:

   Field	Description
   Workspace name	Enter a unique name that identifies your workspace. Names must be unique within the resource group. Use a name that's easy to recall and differentiate from workspaces created by others. The workspace name is case-insensitive.
   Friendly name	This name isn't restricted by Azure naming rules. You can use spaces and special characters in this name.
   Hub	A hub lets you group related workspaces and share resources. If you have access to a hub, select it here. If you don't have access to a hub, leave this blank.

4. If you didn't select a hub, provide the advanced settings. If you selected a hub, these values are taken from the hub.

   Field	Description
   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 one. A resource group holds related resources for an Azure solution. You need the Contributor or Owner role to use an existing resource group. For more information about access, see Manage access to an Azure Machine Learning workspace.
   Region	Select the Azure region closest to your users and data to create your workspace.

5. Select Create to create the workspace.

Create a compute instance

You use the compute instance to run Jupyter notebooks and Python scripts in the rest of the tutorials. If you don't yet have a compute instance, create one now:

1. Select your workspace.

2. On the top right, select New.

3. Select Compute instance in the list.

   

4. Supply a name.

5. Keep the default values for the rest of the page unless your organization's policy requires different settings.

6. Select Review + Create.

7. Select Create.

Quick tour of the studio

The studio is your web portal for Azure Machine Learning. It 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 Authoring section of the studio contains multiple ways to get started creating machine learning models. You can:

  • Notebooks lets you create Jupyter notebooks, copy sample notebooks, and run notebooks and Python scripts.
  • Automated ML guides you through creating a machine learning model without writing code.
  • Designer provides a drag-and-drop way to build models using prebuilt components.

• The Assets section helps you track the assets you create as you run jobs. In a new workspace, these sections are empty.

• The Manage section lets you create and manage compute and external services linked to your workspace. You can also create and manage a Data labeling project here.

Learn from sample notebooks

Use the sample notebooks available in the studio to learn how to train and deploy models. They're referenced in many of the other articles and tutorials.

1. On the left navigation, select Notebooks.
2. At the top, select Samples.

• Use notebooks in the SDK v2 folder for examples that use the current SDK (v2).
• These notebooks are read-only and are updated periodically.
• When you open a notebook, select Clone this notebook at the top to add a copy and any associated files to your Files. A new folder is created for you in the Files section.

Create a new notebook

When you clone a notebook from Samples, a copy is added to your files and you can start running or modifying it. Many of the tutorials mirror these sample notebooks.

You can also create a new, empty notebook and then copy and paste code from a tutorial into it. To do so:

1. Still in the Notebooks section, select Files to go back to your files.

2. Select + to add files.

3. Select Create new file.

   

Clean up resources

If you plan to continue to other tutorials now, skip to Next step.

Stop compute instance

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

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

Delete all resources

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, in the search box, enter Resource groups and select it from the results.

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

3. In the Overview page, select Delete resource group.

   

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

Next step

You now have an Azure Machine Learning workspace that contains a compute instance for your development environment.

Continue to learn how to use the compute instance to run notebooks and scripts in Azure Machine Learning.

Use your compute instance with the following tutorials to train and deploy a model.

Want to jump right in? Browse code samples.