[Django]-Machine Learning (tensorflow / sklearn) in Django?

Asynchronous processing

If you don’t need the classification result from the ML code to pass immediately to the user (e.g. as a response to the same POST request that submtted), then you can always queue the classification job to be ran in the background or even a different server with more CPU/memory resources (e.g. with django-background-tasks or Celery)

A queued task would be for example to populate the field UserResponse.class_name (positive, negative) on the database rows that have that field blank (not yet classified)

Real time notification

If the ML code is slow and want to return that result to the user as soon as it is available, you can use the asynchronous approach described above, and pair with the real time notification (e.g. socket.io to the browser (this can be triggered from the queued task)

This becomes necessary if ML execution time is so long that it might time-out the HTTP request in the synchronous approach described below.

Synchronous processing, if ML code is not CPU intensive (fast enough)

If you need that classification result returned immediately, and the ML classification is fast enough *, you can do so within the HTTP request-response cycle (the POST request returns after the ML code is done, synchronously)

_{*Fast enough here means it wouldn’t time-out the HTTP request/response, and the user wouldn’t lose patience.}