Select target architecture for azureML edge deployment

Hi,

I recently created an Azure ML Model using the ML Designer. The next step for me was to deploy the model on my edge Device.
Creating the image itselft was not the problem (following this totorial https://github.com/Azure-Samples/IoTEdgeAndMlSample/blob/master/AzureNotebooks/01-turbofan_regression.ipynbl). But the default target architecture is amd64 and I want to deploy the modul on an raspberry pi (arm32v7) . Is there a way to configure the Target architecture using the python sdk?

To create the Image i was useing the azureml.core.ContainerImage class. I now this class is deprecated so if u cant provide a solution for this scenario, is there maybe an alternative using the recommended azureml.core.Environment class?

I know that it is possilbel to change the base image but there is no documentation about the images available as far as i know.

Start with the linked AzureNotebooks/01-turbofan_regression.ipynb tutorial and the azureml.core.ContainerImage and azureml.core.Environment entry points. Determine whether ARM32v7 is supported for edge deployment, how target architecture and base images are selected, and what documentation or SDK change would define the supported configuration.

索引モデルが issue の本文から書いたものです。