These replies are all very valuable, so appreciate everyone's comments. I have been involved in implementing a risk model for a financial services company, and

These replies are all very valuable, so appreciate everyone's comments. I have been involved in implementing a risk model for a financial services company, and the system is written completely in R and split as follows: live model: R script listening to redis list, data is json, scores customer applications as they come in; if needed can trivially scale more workers. batch update: also R script, generates model files that live system uses and runs nightly on cron, past models are backed up so rollbacks are possible. The major pain points have been ensuring the live and batch systems match exactly, as we have to rewrite the code to do the same thing on both of them. Unfortunately, since we're doing quite a bit of processing to engineer features from multiple data sources this couldn't easily be expressed as a pipeline in scikit learn. Redis has been invaluable in managing the queues and glueing the different systems together.