I want to setup a decent framework for building and deploying ML code on our servers. The server side code is all java. I have inherited a system that uses
weka as it is based on java and makes life easy for the server side folks. But data scientists who come in, use R/python to do their modelling and then write some glue code to make the models run in java environment on weka. This library is getting outdated(works alright though), but mainly writing this glue code is getting cumbersome/repetitive and can be done away with. I'm looking for an optimized pipeline that helps me get models deployed quickly.
How is this done in the industry? What does your pipeline look like?
I would like to be able to quickly model using one language that data science team uses(I'm tending to python) and be able to deploy/call these models for prediction on the server side without having to redo a bunch of stuff to get it working there.