When a model silently fails in production, the root cause often traces back to a forgotten metadata point: which version of the training pipeline was used. In this episode, Lucas and Luna unpack a real case from a fintech lender that lost two million dollars because a model was accidentally retrained on stale features. They walk through the metadata fields every team should track—from git commit hashes to feature store timestamps—and explain how one well-designed training manifest can save weeks of debugging. No abstract theory: this is the nitty-gritty of model governance that separates amateur setups from production-grade data science.

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