In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

ClearML - Model-Serving Orchestration and Repository Solution

A collection of model deployment library and technique.

Segmenting people on photos using IOS devices [Pytorch; Unet]

Universal Semantic Annotator (LREC 2022)

Chatting-Day's Dialogue State Tracking (DST)

Simple HTTP serving for PyTorch 🚀

A message queue based server architecture to asynchronously handle resource-intensive tasks (e.g., ML inference)

TorchServe images with specific Python version working out-of-the-box.

Serving PyTorch model using flask and docker

A proof-of-concept on how to install and use Torchserve in various mode