Embedding Drift Detection Is Broken by the Curse of Dimensionality

Automated excerpt

TL;DR — Most ML observability stacks still monitor embeddings the way they monitor table columns: one statistical test per dimension. Real embedding observability has to test geometry, not axes. In other words, two embedding spaces can be functionally identical for retrieval purposes while every per-dimension KS test screams drift — and two embedding spaces can be functionally very different for retrieval while every per-dimension test says nothing changed.

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