I would like to raise a concern regarding the deployment process in Azure Machine Learning Studio. Currently, when the same forecast scenario is executed multiple times, the existing deployment on the same endpoint is not automatically removed. This results in deployment failures unless the previous deployment is manually deleted.
I recommend implementing an automated mechanism to delete or overwrite the existing deployment on the endpoint. This would ensure smoother redeployment of updated models and improve overall efficiency in our workflow.
