Deploying a machine-learning model through one centrally hosted service is mostly a solved engineering task. The usual process is familiar: build and test the model, package it in a container, release it, monitor a dashboard, and roll it back when something seems wrong. Deploying the same class of model to a fleet of physically separate…
