Probabilistic programming systems (PPS) define languages that discretize modeling and inference such that any generative model can be easily composed and run with a common inference engine. The main advantage over traditional ML systems in deterministic code (i.e. Python) being concise, modular modeling where the developer doesn't have to write custom inference algorithms for each model/problem. For more info see, for example,  and .
I'm curious though, what applications of PPS are realized in practice? Notably Uber  and Google  are developing/supporting their own (deep learning focused) PPS, but is it known if/how they're used within these companies? Are the frameworks (Pyro  and Edward , respectively) used by other companies?
 Frank Wood (Microsoft) tutorial: https://www.youtube.com/watch?v=Te7A5JEm5UI
 MIT ProbComp lab's page of resources: http://probcomp.csail.mit.edu/resources/