Most data science teams treat documentation as an afterthought—a few comments in a Jupyter notebook or a stale Confluence page. In episode 20 of The Data Science Podcast, Lucas and Luna explore why that approach is dangerous, especially as models move into production. They walk through a real case: a fintech startup whose fraud model silently degraded because feature definitions in the code didn't match the original research notebook. The fix wasn't a better algorithm—it was a living documentation system that versioned assumptions, data schemas, and model cards alongside the code. Lucas explains how tools like DVC and MLflow can enforce this, and why the cost of under-documentation compounds over time. If you've ever inherited a model and wondered 'what was this person thinking?', this episode is for you.

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