I have a situation where I need to train a model in R and then use the model coefficients obtained from that (betas) in order to perform a regression classification in semi-live data. This production system is implemented in pure python (data processing) and django (web interface).
The model coefficients will be calculated every week manually and right now produces a csv, that is read by the python code. I just wanted to know if there are any better ways of doing this?
This is mostly a question on what are the established best practices for cases like this, even though the current approach works.