I'd be very wary of using complex SOUP like TensorFlow, even if brought under my quality system. I think a good answer here is that once one goes under design control the subset of functionality needed should be implemented in-house under the organization's SDLC.
Of course these things are meant to be used (1) to train the system, (2) as a player in the prototype. Exactly like in the old school ML-based systems: you train in Matlab or CudaConvNet, and then you load the trained classifier into the custom-made player highly tuned to your hardware and problem domain.
