Machine Learning Studio

Getting Started with Azure Machine Learning Setup Guide

Follow the steps below to create your ML Studio resource.

Prerequisites

Before you begin, ensure you have:

  • An active Microsoft Azure subscription
  • Appropriate permissions to create resources in your Azure subscription

Step 1: Navigate to Azure Machine Learning

Begin by accessing the Microsoft Azure main platform. In the search bar at the top of the Azure portal, type "machine learning" and select "Azure Machine Learning" from the Services section in the search results.

Azure portal search showing machine learning service

Searching for Azure Machine Learning in the Azure portal

Step 2: Create a New Workspace

Once you're in the Azure Machine Learning service page, locate and click the "+ Create" button. From the dropdown menu that appears, select "New workspace" to begin the workspace creation process.

Azure ML service page with Create button highlighted

Creating a new workspace from the Azure ML service page

Step 3: Configure Your Workspace

You'll now be presented with the workspace creation form. Complete the following configuration:

Basic Settings:

  • Subscription: Select your Azure subscription
  • Resource Group: Create a new resource group or select an existing one in your preferred region
  • Workspace Name: Choose a descriptive name for your ML workspace
  • Region: Select your preferred Azure region (choose one close to your location for optimal performance)

Associated Resources:

For the following resources, click "Create new" to ensure your notebooks and data are stored separately from any previous resources:

  • Storage Account: This will store your datasets, models, and experiment outputs
  • Key Vault: Securely manages secrets, keys, and certificates
  • Application Insights: Provides monitoring and logging capabilities
Azure ML service page with Create button highlighted

Configuring your workspace

Note: You don't need to modify any settings in the subsequent configuration tabs (Networking, Advanced, Tags, etc.) unless you have specific organisational requirements.

Step 4: Deploy and Launch

After reviewing your configuration settings, click "Review + Create" and then "Create" to deploy your workspace. The deployment process typically takes 2-3 minutes.

Once the deployment is complete, you'll see a confirmation message. Click "Go to resource" to access your new workspace, then click "Launch studio" to open the Azure Machine Learning Studio interface.

Deployed workspace with Launch studio button

Launching the ML Studio from your deployed workspace

Next we'll run through a quick tour of the platform