Data Drift: How Models Silently Decay and What Teams Do About It
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In this episode, Lucas and Luna unpack the quiet killer of machine learning systems: data drift. They trace how models trained on 2024 data start stumbling by 2026, using a concrete example from retail demand forecasting where a model quietly degraded over eighteen months. The conversation walks through the difference between covariate shift and concept drift, why traditional retraining schedules often miss the real problem, and how modern ML teams use statistical tests like the Kolmogorov-Smirnov test and population stability index to catch drift before it hits the bottom line. They also get into the practical side: what a drift dashboard actually looks like, who owns the alert response, and why feature stores with built-in drift detection are becoming the new standard. If you build or manage ML systems, this episode gives you a clear mental model for why models age and what you can do about it. #DataDrift #ConceptDrift #CovariateShift #MachineLearning #ModelMonitoring #MLOps #FeatureStores #DataScience #RetailAnalytics #KolmogorovSmirnov #PopulationStabilityIndex #DriftDetection #ModelRetraining #DataQuality #Technology #FexingoBusiness #BusinessPodcast #DataPodcast Keep every episode free: buymeacoffee.com/fexingo
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