Create an Azure Machine Learning workspace

This quickstart guides you through creating an Azure Machine Learning workspace. An Azure Machine Learning workspace is a cloud-based resource that provides a centralized location to manage all your Azure Machine Learning artifacts, such as datasets, experiments, models, and compute resources.

Note: This article uses the Azure CLI for demonstration. You can also use the Azure portal, Azure PowerShell, or SDKs to create a workspace.

Prerequisites

Before you begin, make sure you have:

An Azure subscription. If you don't have one, create a free account before you begin.
The Azure CLI installed. For installation instructions, see Install the Azure CLI.

Sign in to Azure

az login

Follow the on-screen instructions to authenticate.

Create a resource group

A resource group is a logical container for your Azure resources. Create a new resource group for your workspace:

az group create --name myResourceGroup --location eastus

Replace myResourceGroup with a unique name for your resource group and eastus with your desired Azure region.

Now, create your Azure Machine Learning workspace:

az ml workspace create --name my-workspace --resource-group myResourceGroup --location eastus

Replace my-workspace with a unique name for your workspace.

You can optionally specify other parameters like --storage-account or --vnet for advanced networking scenarios.

Verify the workspace creation

After the command completes, you can verify the workspace creation by listing your workspaces:

az ml workspace list --resource-group myResourceGroup

This command will output details about the workspace you just created.

Tip: You can find your workspace details, including its URL and associated resources, in the Azure portal.

Next Steps

Congratulations! You have successfully created an Azure Machine Learning workspace. Now you can: