I trained a model that performed really well during experimentation and validation.The metrics looked solid, and nothing seemed off in the notebook.However, once deployed, predictions started becoming unreliable within days.I’m struggling to understand why production behavior is ...
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Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
Training data looks correct.Live predictions use the same features by name.Yet values don’t match expectations. This undermines trust in the system?
Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.
I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
My production data is unlabeled.I can’t calculate accuracy or precision anymore.Still, I need to know if the model is degrading.What can I realistically monitor?
Overall metrics look acceptable.But certain users receive poor predictions.The issue isn’t uniform. It’s hard to detect early?
Different teams trained models independently.Each performs well in certain cases.Now deployment is messy.Choosing one feels arbitrary.
The model still runs without errors.Performance seems “okay.”But I suspect it’s getting stale.There’s no obvious trigger.
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.