
Once you have trained your model, it needs to be integrated into a larger system to have a full-fledged application, we use the term “model serving” to refer to this integration. Basically model serving is making your trained model available to run inferences and subsequent use of the model. 
Torchserve is the Pytorch preferred solution for serving models in production. It is a performant and scalable tool that wraps your model in a HTTP or HTTPS API. It has a frontend implemented in Java that handles multiple tasks from assigning workers for serving models to handling the connection between client and server. Torchserve has a Python backend that is responsible for handling the inference service.