Title: 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&#x27;t have to write custom inference algorithms for each model&#x2F;problem. For more info see, for example, [1] and [2].<p>I&#x27;m curious though, what applications of PPS are realized in practice? Notably Uber [3] and Google [4] are developing&#x2F;supporting their own (deep learning focused) PPS, but is it known if&#x2F;how they&#x27;re used within these companies? Are the frameworks (Pyro [5] and Edward [6], respectively) used by other companies?<p>[1] Frank Wood (Microsoft) tutorial: https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Te7A5JEm5UI<p>[2] MIT ProbComp lab&#x27;s page of resources: http:&#x2F;&#x2F;probcomp.csail.mit.edu&#x2F;resources&#x2F;<p>[3] https:&#x2F;&#x2F;eng.uber.com&#x2F;pyro&#x2F;<p>[4] https:&#x2F;&#x2F;medium.com&#x2F;tensorflow&#x2F;introducing-tensorflow-probability-dca4c304e245<p>[5] http:&#x2F;&#x2F;pyro.ai&#x2F;<p>[6] http:&#x2F;&#x2F;edwardlib.org&#x2F;
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