Small changes break unexpected parts of a Flow. Testing feels risky. I want to understand why Flows become fragile.
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Flows worked before deployment but not after. I want to understand why.
I retrained my model with more recent data.The assumption was that newer data would improve performance.Instead, the new version performs worse in production.This feels counterintuitive and frustrating.
As usage increased, we started seeing more UNABLE_TO_LOCK_ROW errors. These rarely occurred earlier but are now fairly common during updates and integrations. The logic hasn’t changed much. I want to understand why locking becomes more frequent over time.
Feature distributions look stable.But prediction quality is declining.Simple drift metrics don’t explain it.Something deeper seems wrong.
