Lucas and Luna dive into concept drift—when the statistical properties of a target variable change over time, degrading model performance. Using a concrete case from a credit card fraud detection system, Lucas explains how data scientists monitor for drift using metrics like PSI (Population Stability Index) and KL divergence. Luna challenges whether traditional thresholds are enough, and they explore adaptive strategies like online learning and retraining triggers. Specific examples from e-commerce and finance show how drift detection prevents silent failures. By the end, listeners understand the difference between covariate shift, prior probability shift, and concept shift—and why monitoring is a continuous, not one-time, task.

#DataScience #MachineLearning #ConceptDrift #MLOps #ModelMonitoring #FraudDetection #PopulationStabilityIndex #KullbackLeiblerDivergence #OnlineLearning #CovariateShift #PriorProbabilityShift #ConceptShift #Retraining #FeatureDrift #PredictionDrift #Technology #FexingoBusiness #BusinessPodcast

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