Model deployment is an important but still tiny part of the overall ranking/recommendation systems. Bulk of the complexity stems from two key properties of recommendation systems (which are different from say computer vision models):
1. The system operates on user feedback. As a result, it needs to manage flow of lots of data, with at least some subset being managed in realtime.
2. For any single request, there are thousands of things to recommend from. As a result, a single request is not scoring a single ML model but thousands of models - one (or often more, see value modeling the post) for each candidate.
Good feedback, noted. Will get the next post focused on training within the next couple of days.