Deep-Learning-in-Production

Why we included this project

When you've trained a model and need to get it out of the notebook and into a running service, this repository is a shortcut through the usual confusion. The author has sorted references by the steps teams actually take: converting PyTorch models, moving them to C++ or browser runtimes, and building REST APIs with Flask or TorchServe. Each link gets a short note, so you can quickly tell official docs from community posts that are worth reading. The collection also covers serving options like ONNX, TensorFlow Serving, LibTorch, and serverless functions, which is useful if you're deciding between them for an internal project. It's a reading guide, not a deployable framework, so you get pointed at working code examples without a runtime to maintain.