Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
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My model works well during training and validation.But inference results differ even with similar inputs.There’s no obvious bug in the code.It feels like something subtle is off.
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.
An old model is still running in production.Traffic has shifted to newer versions.I want to remove it safely.But I’m worried about hidden dependencies.