@jclemens1
Thanks a lot! I'll make sure to try rstudio connect and if it works hopefully I'll convince my bosses to buy it - we are just starting with R in the company so it's a new thing.
@raybuhr
Raymond, what a great blogpost!! I am looking forward to the next instalment!!
I will try plumber out at work now, and see if I can expose my models as API's for everyone to enjoy 
So you think that if I only return prediction (i.e., no training/processing whatever, just predict(...)), plumber + docker running on AWS would be sufficient in terms of responsiveness? Perhaps I should just try it out and see how it goes. Do you have a lot of requests to your API's at work?
Since our data lives in a database on our servers, another thing I am considering is first starting with the simple approach of deploying the model by pulling data from the DB, predict and then return the predictions to the DB as a table (the batch processing approach I was mentioning), and then in time deploy the model as an API. In this respect, would you suggest to keep training and prediction in two different servers? I was thinking of predicting new scores every night when new data from the day comes in, and to retrain the model say every week to start with.
Ciao,
R.