Episode 27 of The Data Science Podcast dives into one of the most common yet under-discussed problems in applied machine learning: production data pipeline failures. Lucas and Luna unpack a real-world case from a mid-sized fintech company where a silent schema change broke a credit risk model for three weeks, costing over $2 million in mispriced loans. They walk through the specific failure modes—schema drift, late-arriving data, and upstream dependency breaks—and discuss practical countermeasures like schema enforcement, data quality monitors, and pipeline observability tools. If you've ever had a model silently degrade in production, this episode will give you a framework to diagnose and prevent it.

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