Machine Learning Studio Error (530 No backend service has been configured...)

Question

I just created an Azure Machine Learning workspace (new resource group, workspace, storage, etc.) and when I attempt to launch ML studio (ml.azure.com) I get the error below. I have tried both public access enabled from all and selected IP with my public IP. Not sure what else to check. Has anyone encountered this before?

{
    "error": {
        "code": "UserError",
        "severity": 4,
        "message": "No backend service has been configured to receive requests for this URI.",
        "messageFormat": null,
        "messageParameters": null,
        "innerError": {
            "code": "NotFoundError",
            "innerError": null
        }
    },
    "statusCode": 530
}

Answer 1

Hi Randy Sauers,

Summarizing the issue:

Problem: Machine Learning Studio Error 530 blocking access and page load.
Re-solution: It was an internment issue fixed as of Dec 3rd 20:55 UTC.
Recommendation: Clear your browser cache to get to the page.

Thanks to Microsoft employee bandsina for providing confirmation on fix.

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Please "Accept the answer and upvote" to help other community members looking for similar remediation.

Answer 2

The error message you are encountering, "No backend service has been configured to receive requests for this URI," typically indicates that there may be an issue with the configuration of your Azure Machine Learning workspace or the associated services. Here are some steps you can take to troubleshoot this issue:

1. Check Resource Providers: Ensure that the necessary resource providers for Azure Machine Learning are registered in your subscription. You can check this in the Azure portal under "Resource providers". The key providers include:
   • Microsoft.MachineLearningServices
   • Microsoft.Storage
   • Microsoft.ContainerRegistry
   • Microsoft.KeyVault
2. Verify Workspace Configuration: Make sure that your workspace is correctly configured and that all required resources (like storage accounts) are properly set up and accessible.
3. Access Control: Ensure that your user account has the necessary permissions to access the Azure Machine Learning workspace. You may need to add roles such as "Contributor" or "Owner" to your user account.
4. Public Access Settings: Since you mentioned trying both public access options, double-check that the settings are correctly applied and that there are no restrictions preventing access.
5. Diagnostics: You can run diagnostics on your workspace from Azure Machine Learning studio or the Python SDK to identify any detected problems and possible solutions.

If you continue to experience issues after these checks, consider reaching out to Azure support for further assistance.

---

References:

• Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)
• Manage Azure Machine Learning workspaces by using Terraform
• Manage Azure Machine Learning workspaces by using Azure CLI
• Troubleshoot online endpoint deployment and scoring
• Troubleshoot problems during creation of a data labeling project

Answer 3

I am still experiencing a 503 error when trying to access Azure Machine Learning Studio (Launch Studio). Although the platform was reported as “mitigated,” the issue continues only for my user.

Below is a summary of all the technical validations I performed:

1. Roles and Permissions
   • Confirmed my user is part of the DEVOPS group, which has:

     Owner (inherited at subscription level)
     
           Contributor
     
                 Azure AI Administrator
     
                       AzureML Compute Operator
     
                             AzureML Data Scientist
     
                             The workspace has permissions assigned to Storage, Key Vault, ACR, and Application Insights.
     
                             I also tested assigning roles directly to my user → same result.
     
                             **Diagnostic Settings and Logs**
     
                                Enabled all Azure ML diagnostic logs (AmlCompute*, AmlRun*, AmlDataStore*, etc.).
     
                                   Checked Log Analytics — no backend errors explaining the 503.
     
                                      No compute cluster or provisioning failures found.
     
                                      **Identity / Tenant Issues**
     
                                         Ran Azure Entra Diagnostic tool.
     
                                            It detected some sign-in attempts being blocked by Conditional Access.
     
                                               Verified my user **is a Member** of the FinAIPro tenant (not Guest).
     
                                                  Switched directories manually → same behavior.
     
                                                  **Conditional Access**
     
                                                     No visible CA policies blocking access.
     
                                                        Validated Security Defaults and authentication requirements.
     
                                                        **Network / VPN**
     
                                                           I am not behind a VPN.
     
                                                              The workspace has **public network access enabled**.
     

Despite all these checks, the 503 error persists only for my account.

I kindly request support to review the following:

• If there are hidden Conditional Access rules or inherited tenant policies blocking my identity.

Whether my home tenant is being misinterpreted by Azure ML.

If a token refresh or cleanup of previous identity associations is required at the service level.

I am still experiencing a 503 error when trying to access Azure Machine Learning Studio (Launch Studio).
Although the platform was reported as “mitigated,” the issue continues only for my user.

Below is a summary of all the technical validations I performed:

Despite all these checks, the 503 error persists only for my account.

I kindly request support to review the following:

If there are hidden Conditional Access rules or inherited tenant policies blocking my identity.

Whether my home tenant is being misinterpreted by Azure ML.

If a token refresh or cleanup of previous identity associations is required at the service level.