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MLOpsAWShard~25 min

Excellent offline, mediocre live, no error anywhere

A churn model scores 0.91 AUC on a held-out set built with a point-in-time-correct feature store query. Deployed behind an API, its live decisions perform at roughly the level of the previous heuristic — a large, consistent gap with the offline number.

Feature values logged at serving time are within their expected ranges. No nulls above the usual rate. The model artefact's hash matches the one evaluated offline. Input schema validation passes on every request.

Training features were computed in a Spark job over the warehouse. Serving features come from a Redis online store populated by a separate streaming job.

The artefact is identical and the inputs validate. Give me your ranked hypotheses for the offline-online gap, and for each the comparison you would run to confirm it.

Choose how you want to be interrogated

The same case under two modes is two different exercises. If you are unsure, RCA drill is the one that trains root-cause analysis most directly.

Before you start: you begin locked. The Mentor will give you nothing — not a nudge, not a category — until you state a specific position and the mechanism you think produces it. Four genuine attempts unlock the resolution. There is no shortcut.