Azure Machine Learning Workspace

A comprehensive guide to setting up your cloud-based ML environment.

Creating Your First Azure Machine Learning Workspace

An Azure Machine Learning workspace is the foundational resource for any machine learning project in Azure. It provides a centralized location to manage your ML experiments, data, models, and compute resources.

Prerequisites

  • An Azure subscription. If you don't have one, you can create a free account.
  • Permissions to create resources within your Azure subscription.

Steps to Create a Workspace

1

Sign in to the Azure portal

Navigate to the Azure portal and sign in with your Azure account credentials.

2

Create a new resource

On the Azure portal dashboard, click + Create a resource. In the search bar, type "Machine Learning" and select Azure Machine Learning from the results.

Alternatively, you can find it under AI + Machine Learning.

3

Configure workspace details

Click Create to start the workspace creation process. You'll need to provide the following information:

  • Subscription: Select your Azure subscription.
  • Resource group: Choose an existing resource group or create a new one. A resource group is a logical container for your Azure resources.
  • Workspace name: Enter a unique name for your workspace (e.g., my-ml-workspace).
  • Region: Select the Azure region closest to you or your users for optimal performance.
  • Storage account: You can select an existing general-purpose storage account or let Azure create a new one.
  • Key vault: Similar to the storage account, you can select an existing or let Azure create a new one for managing secrets.
  • Application Insights: Choose an existing or let Azure create a new one for monitoring.
  • Container registry: Optionally, link an Azure Container Registry to store Docker images used by your ML solutions.
4

Review and create

After filling in the details, click Review + create. Azure will validate your settings. Once validated, click Create to deploy your workspace.

Important:

The creation process might take a few minutes. You will be notified when the deployment is complete.

Accessing Your Workspace

Once the deployment is successful, you can access your workspace by navigating to it from the Azure portal dashboard or by searching for its name. The workspace overview page provides access to all Azure Machine Learning services, including Notebooks, Datasets, Experiments, Models, Endpoints, and Compute instances.

Launch Workspace in Azure Portal View SDK Documentation

Next Steps

  • Create a Compute Instance: Set up your first compute resource for training and development.
  • Register a Dataset: Import your data into the workspace.
  • Create an Experiment: Start tracking your machine learning runs.