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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.
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The batch prediction job used to run in minutes.As data volume increased, runtime started doubling unexpectedly.Nothing changed in the model code itself.Now it’s becoming a bottleneck in the pipeline.
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.
Predictions affect business decisions.Stakeholders ask “why” a lot.Raw probabilities aren’t helpful.Trust is fragile.
The model still runs without errors.Performance seems “okay.”But I suspect it’s getting stale.There’s no obvious trigger.