Commit to auditing feature availability against the prediction moment.
A model feature list, training dataframe, or feature PR with at least one engineered input that lacks a prediction-time availability note. Use the next churn, fraud, lead-scoring, or risk-model feature artifact where reviewers are already discussing validation lift. I will add a prediction-time audit table to [feature artifact] with feature_name, score timestamp, source timestamp, availability status, and redesign note before the next model review. In the next feature-engineering PR where a training dataframe adds or changes model inputs Before the next model-review notebook where validation lift is discussed for newly engineered features When a teammate suggests reusing a feature view,…
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