Approximate inference, calibration, latency, and monitoring all have to come together for Bayesian uncertainty to be useful in production. This article walks through what it takes to move from a method that works in a notebook to one a system can depend on.
Research Translation
What Production-Grade Bayesian Inference Requires
Bayesian methods give a model a principled sense of its own uncertainty. Making them run reliably, at scale, and within a latency budget is a translation problem, and it is where many promising ideas stall.