Model Packaging and ONNX

A model trained in one framework, PyTorch, scikit-learn, XGBoost, is normally only easy to run inside that same framework, which becomes a problem when the serving environment wants something faster or written in a different language, say a low-latency C++ execution engine. ONNX (Open Neural Network Exchange) solves this by defining a common file format that represents a model as a graph of standard mathematical operations, independent of the framework that produced it. Most major training frameworks can export to ONNX, and a range of runtimes, notably ONNX Runtime, can load that file and run inference on CPU or GPU without ever needing the original training code installed.

The practical benefit is decoupling: a research team can keep training in whatever framework is most productive, then hand a single portable .onnx file to a production team that serves it with a runtime optimized for latency, without maintaining a live dependency on the training library's version in production. The tradeoff is that not every custom layer or operation converts cleanly, exotic architectures sometimes need custom ONNX operators or don't convert at all, so packaging to ONNX is usually a validated step in the deployment pipeline, with outputs checked against the original model before it's trusted in production.