Hi,
It appears to me that the types of models that can be registered using the azure SDK should come from sklearn, Keras, etc and be converted into a pickle file and after registration, you can send the request to the endpoint.
I can think of a very limited number of cases where you just want to create a backend system that only returns the model prediction. In most cases, the backend of a front end application would take care of all the calculations and sometimes also decide which models to run and maybe combine the results before sending the output to the frontend. For this reason, you might want to implement the complex logic in rest API such as Flask. In this case, why would I call another endpoint from the flask application to run the model, instead of simply loading the pickle file in the flask project?
What are the cases where you are registering with Azure endpointst and actually using the endpoints to make a prediction in azure from you trained model? Are you limited to specific libraries to create the pickles? what if you need to process the input before making the predictions?
thanks