regression model learning path, error: failed to mount datastore

Question

I'm trying to go through this learning path (https://microsoftlearning.github.io/AI-900-AIFundamentals/instructions/02a-create-regression-model.html), but when I get to the step to submit the model, I am getting the following error. I've updated authentication on my workspace blob storage, and on the azureml_globaldatasets. Not sure what to do?

UserErrorException:
	Message: Failed to mount datastore 'azureml_globaldatasets' for DataReference 'INPUT_Dataset'. Datastore 'azureml_globaldatasets' has credential type 'Sas'. Please make sure the valid sas token is registered in this Datastore..
	InnerException None
	ErrorResponse 
{
    "error": {
        "code": "UserError",
        "message": "Failed to mount datastore 'azureml_globaldatasets' for DataReference 'INPUT_Dataset'. Datastore 'azureml_globaldatasets' has credential type 'Sas'. Please make sure the valid sas token is registered in this Datastore.."
    }
}

Answer 1

@gspross Thanks for the question. The error message suggests that there is an issue with the SAS token for the azureml_globaldatasets datastore. The SAS token is used to authenticate access to the datastore, and if it is invalid or expired, you may encounter errors when trying to access the datastore.

To resolve this issue, you can try the following steps:

Check if the SAS token for the azureml_globaldatasets datastore is still valid. You can do this by going to the Azure portal, navigating to the storage account associated with the datastore, and checking the SAS token settings.

If the SAS token is invalid or expired, generate a new SAS token with the required permissions and update the datastore credentials in your Azure Machine Learning workspace.

If the SAS token is valid, try refreshing the datastore credentials in your Azure Machine Learning workspace**1**. You can do this by going to the Datastores tab in your workspace, selecting the azureml_globaldatasets datastore, and clicking the Refresh button.

If refreshing the credentials does not work, try deleting and recreating the azureml_globaldatasets datastore in your workspace. Make sure to use the correct SAS token and credentials when creating the datastore.