How would you design a system to detect training-serving skew using model registry metadata?
This tests statistical monitoring between production data and registry training baselines. Strong answers: schema-bound metadata, incremental stats, drift metrics PSI, tiered alerting. Red flag: schema validation mistaken for drift or manual checks only.
This tests architecting a production monitoring loop comparing inference distributions against training baselines in a model registry. A strong design covers four layers: immutable schema-bound metadata with per-feature training statistics; a logging pipeline writing production features to a warehouse; scheduled or streaming jobs computing distribution distances like PSI or KS-test; and tiered alerting with automated rollback or shadow fallback.
Read the original → cloud.google.com
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