Just to add onto this feedback (because I think /u/JackBlemming is 100% correct) you would probably benefit from storing some of the most popular datasets (ImageNet, MS COCO, whatever is relevant to the fields you're targeting) somewhere in the cloud where you can provide fast read access (or fast copies) to any number of training workers that get spun up.

Research datasets tend to be fairly standardized so I think you could get a high amount of coverage by just having a few common datasets available. I only gave computer vision examples because that's what I'm most familiar with but if you get a few CV datasets, a few NLP ones etc. you should be able to provide a killer UX.

Bonus points if you're somehow able to configure the repos to read from the centralized datastore properly automatically (though this is probably difficult/impossible).

/u/psychorameses was spot on when he said that python is a scripting language. It’s really not meant for you to be able to do this.

The correct answer for this is what /u/robertknight2 suggested. If you want to deploy a production grade application then you need to export your model (either to ONNX, or TFLite or similar). Once you have the exported model you write a GUI application any old way. You can make a QT GUI application that interacts with the ONNX model using the C++ ONNX runtime API. Or you could write an electron application that uses TFJS to run a TFLite model. Both are viable options (though idk if you’ll be able to get TFLite using the GPU easily in Electron).

This is CPU only but here is an example of a C++ windows application that uses the ONNX runtime. Packaging CUDA (TensorRT really) and getting it to work with ONNX should only be a little bit more work. A quick google search yielded this thread for packaging TensorRT into a VS project on windows. It looks like it has a working example too: https://github.com/NVIDIA/TensorRT/issues/2085