Azure Machine Learning Now Available in West Central US

This post is authored by Ted Way, Senior Program Manager at Microsoft.

We are pleased to announce that Azure Machine Learning is now generally available in the West Central United States (WCUS) Azure region. With this, Azure ML is now available in these regions:

  • United States
    • East US2
    • South Central US
    • NEW: West Central US
  • Europe
    • West Europe
    • Germany Central
  • Asia Pacific
    • Japan East
    • Southeast Asia

Migrating Experiments 

You can create new workspaces and experiments in WCUS, of course. If you have experiments in other regions that you now wish to migrate to WCUS, you can use the Copy-AmlExperiment cmdlet in PowerShell or publish an unlisted experiment in the Gallery (in the documentation, search for “have it only accessible to people with the link”). This will provide access to your experiment only to the people you share the URL with. Click on the link to get to the experiment, and then click “Open in Studio” to choose to “West Central US” region.


If you use Free or Guest Access workspaces, they will continue to be created and operated out of the South Central US region.

Deploying Web Services

Whether you desire a closer location to reduce latency or take advantage of another region in North America for high availability, you now have the option to run your web services in WCUS. If you do not want to migrate your experiment but only wish to deploy a web service to WCUS, open a Studio workspace running in any region and create a predictive experiment. Click “Deploy Web Service” and select “Deploy Web Service [New] Preview.”


Once you are in the new web service management portal (in preview), select “Web Services” at the top. Click on the web service you want to copy, and then select “Copy” in the tab. Choose “West Central US” as the region, and then click the “Copy” button. This will create a copy of the web service in WCUS that you can then use just like any other Azure ML web service.


We look forward your feedback or comments at the Azure ML forum.

Ted
@tedwinway