5 Steps to Create an Azure Machine Learning Workspace

Microsoft Azure Machine Learning is a cloud-based platform for building and operating machine learning solutions in Azure.

Photo by Volodymyr Hryshchenko on Unsplash

Create an Azure Machine Learning workspace

To use Azure Machine Learning, you create a workspace in your Microsoft Azure subscription. Within Azure ML you can manage data, compute resources, code, models, and other artifacts related to your machine learning workloads.

After completing your activities, be sure to follow the cleaning instructions to stop computing resources.

Follow these steps to create a workspace:

1. Sign into the Microsoft Azure portal using your Microsoft credentials.

https://portal.azure.com/

2. Select＋Create a resource, search for Machine Learning, and create a new Machine Learning resource with the following settings:

1. Select + Crete a resource

2. Search for Machine Learning

3. Create a Machine Learning resource

4. Input the information related to the resource, following settings below

• Subscription: Your Azure subscription
• Resource group: Create or select a resource group
• Workspace name: Enter a unique name for your workspace
• Region: Select the geographical region closest to you
• Storage account: Note the default new storage account that will be created for your workspace
• Key vault: Note the default new key vault that will be created for your workspace
• Application insights: Note the default new application insights resource that will be created for your workspace
• Container registry: None (one will be created automatically the first time you deploy a model to a container)

At the end click the button Review + create, and next Create

3. Wait for your workspace to be created (it can take a few minutes). Then go to it in the portal.

1. Creating Machine Learning Services

2. When the deployment is complete, click on Go to resource

4. On the resource page, launch Microsoft Azure Machine Learning Studio (or open a new browser tab and navigate to https://ml.azure.com), and sign into Azure Machine Learning studio using your Microsoft account.

1. Resource page

2. Click on Launch studio

5. In Azure Machine Learning Studio, select your:

• Current Directory
• Current Subscription
• Current Workspace

Click on Submit, when the home page of Microsoft Azure Machine Learning Studio is loaded, toggle the ☰ icon at the top left to view the various pages in the interface. You can use these pages to manage the resources in your workspace.

You can manage some settings of your workspace using the Azure portal, but for data scientists and machine learning engineers, Azure Machine Learning Studio provides a more focused user interface for managing workspace resources

Remember: After completing your activities, be sure to follow the cleaning instructions to stop computing resources.

These instructions are part of the Microsoft Azure Machine Learning course on Coursera, reference link below.

Microsoft Azure Machine Learning

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