As part of the Unity SynthDet Viewer App project, we found it best to host a trained ML model running in PyTorch in the cloud, and expose a REST API to communicate with a mobile device used for capturing images. For hosting this REST API, we went with TorchServe. TorchServe takes an ML model, runs it in PyTorch, and exposes an endpoint used for predictions.