Looking for example infrastructure stacks/workflows/pipelines
In order to understand the variety of ways machine learning can be integrated into production applications, I think it is useful to look at open source projects and papers/blog posts from companies describing their infrastructure.

The common theme that these systems have is the separation of model training from model application. In production systems, model application needs to be fast, on the order of 100s of ms, but there is more freedom in how frequently fitted model parameters (or equivalent) need to be updated.

People use a wide range of solutions for model training and deployment:


Build a model, then export and deploy it with PMML


  
  AirBnB describes their model training in R/Python and deployment of PMML models via OpenScoring.
  Pattern is project related to Cascading that can consume PMML and deploy predictive models.
  

Build a model in MapReduce and access values in a custom system


  
  Conjecture is an open source project from Etsy that allows for model training with Scalding, an easier to use scala wrapper around MapReduce, and  deployment via Php.
  Kiji is an open source project from WibiData that allows for real-time model scoring (application) as well as functioanlity for persisting user data and training models on that data via Scalding.
  

Use an online system that allows for continuously updating model parameters.


  
  Google released a great paper about an online collaborative filtering they implemented to deal with recommendations in Google News.