Assemble a training dataset using point-in-time retrieval rather than current snapshots.
The lead-score model needs a training dataset for historical opportunities without leaking current account state into past scoring moments. Point-in-time dataset flow: entity dataframe -> feature service -> historical retrieval -> miss inspection -> sampled eligibility check The common shortcut is joining every feature from the latest warehouse snapshot because it is simpler and often produces a better offline metric. Before SELECT opportunities.*, accounts.current_plan, activity.last_14d FROM warehouse tables WHERE quarter = Q1. After Entity rows include account_id and score_at. Historical retrieval joins only feature values observed before score_at and within the feature TTL; missing past values remain missing and are…
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