First?in, first?out labeling hid model gaps; labeling queues tied to MLflow Registry and an impact?driven prioritizer
Labeling ran first?in/first?out with no link to model needs, so effort missed high?impact gaps and training drifted. Intelligex tied queues to performance dashboards and added a machine?learning prioritizer selecting clips by expected gain. Teams labeled what mattered, training steadied, and product managers cited shared evidence.

