Transparency and non-executable export format is a serious thing, take a look at https://embracethered.com/blog/posts/2022/machine-learning-a....
After version 0.9 release, PyMilo became feature-completed with full support of scikit-learn models, now it's time to move on to PyTorch and then Tensorflow. But we decided to add the "ML Streaming" feature before getting into PyTorch, in order to provide an easy way to smoothly stream your ML model. By using the "ML Streaming" feature you can easily deploy your model into the remote server, connect to it from the client side, and choose the working mode, either delegation or local mode, through delegation mode your requests will be relayed to the remote server and you can easily work with your remote model from any devices without any further dependencies, and finally, you can download model for local use.
We will release the 1.0 (tenth) version of PyMilo around Sep 16th, this release will be the first release to have the "ML Streaming" feature with support of REST API, and we will next add other protocols such as Websocket.
Here is PyMilo: https://github.com/openscilab/pymilo Thank you for your time