Real-time fraud detection with Feature Views
Summary
Feature Views let you define fraud features once and reuse them for both point-in-time training and online scoring, which reduces data leakage and training-serving skew. The example pairs rolling windows for payment bursts with a 30-day Sawtooth baseline and an on-demand feature that scores each payment against it, and a single materialize_features() call sets up managed streaming pipelines into the Online Feature Store on Lakebase with sub-200ms feature freshness.
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