Kind of an old post, but I like the idea, so let me try to give it some love 
Yes, at my company I am working on bringing a fire-prediction ML solution into production. The prediction service is part of an IoT implementation in a large forestry area. We gather on the ground data with IoT sensors and collect the data. The prediction service runs as its own docker container which retrieves data from the Prometheus DB and runs it through a classifier and regressor. The latter two are trained by the training container, once every few months with updated data.
The results are shown to the customer (government forestry org.) by means of Grafana Dashboard.
All in all, training the models was 20% of the work, engineering it into production took way more effort.