In this lab, 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.
An Azure account with an active subscription. Create an account for free.
What is a 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. The compute resources provide a pre-configured cloud-based environment you can use to train, deploy, automate, manage, and track machine learning models.
- Sign in to the Azure portal by using the credentials for your Azure subscription.
- To create Azure Machine Learning resource click here
- 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 amlpt-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 amlpt-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. |
- After you're finished configuring the workspace, select Review + Create.
- 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.
- To view the new workspace, select Go to resource.
- From the portal view of your workspace, select Launch studio to go to the Azure Machine Learning studio.
You could install Azure Machine Learning on your own computer. But in this workshop, 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.
- On the left side, select Compute.
- Select +New to create a new compute instance.
- Supply a name
- Select CPU and select "Standard_DS3_v2" as virtual machine size
- 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.
Quickstart: Create workspace resources you need to get started with Azure Machine Learning Read more on Docs
Your development environment in your Azure Machine Learning environment is now ready to use. You can continue to the next lab.
In this lab you have:
- Created an Azure Machine Learning Workspace
- Created a Compute Instance in your Azure Machine Learning Workspace