Machine learning models don't break the way software does. They rot slowly, like fruit left on the counter. In this episode, Lucas and Luna explore a real-world case from a fintech lending company that deployed a fraud detection model in late 2024. By February 2026, the model's precision had dropped from 92% to 61% — not because of a bug, but because borrower behavior shifted. This is data drift: the gap between training data and live data. Lucas explains the two types — covariate shift and concept drift — and walks through the fintech's post-mortem. They discuss detection methods, monitoring dashboards, and the hard decision to retrain or rebuild. Luna asks the crucial question: if drift is inevitable, why don't more teams bake monitoring into their MLOps pipeline from day one? By the end, listeners understand why drift is the silent killer of production models — and how to spot it before it costs real money.

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