How Data Teams Use Embedding Drift to Catch Model Decay

In this episode of The Data Science Podcast, Lucas and Luna explore how data teams use embedding drift to detect when machine learning models silently decay in production. They start with a concrete example: a fraud model at a large bank that started missing new scam patterns after a year in production. The conversation covers what embeddings are, why monitoring drift in embedding space is more sensitive than tracking raw feature distributions, and practical methods like cosine similarity shifts and centroid movement. They also discuss how teams can set alert thresholds without drowning in false positives, and the trade-off between sensitivity and actionability in drift detection. The episode closes with a reflection on how embedding drift monitoring ties into broader ML observability and the importance of treating models as living systems that need continuous care. #EmbeddingDrift #ModelMonitoring #MachineLearning #DataScience #MLOps #FraudDetection #ModelDecay #FeatureDrift #CosineSimilarity #ProductionML #DataMonitoring #AI #Technology #Business #FexingoBusiness #BusinessPodcast #DataSciencePodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo