I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
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The model still runs without errors.Performance seems “okay.”But I suspect it’s getting stale.There’s no obvious trigger.
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 ...
Predictions are made in real time.Ground truth arrives much later.Immediate accuracy monitoring isn’t possible.I still need confidence the model is healthy.
Batch predictions look reasonable.Real-time predictions don’t.Same model, same features—supposedly. Yet results differ?
My deployed model isn’t crashing or throwing errors.The API responds normally, but predictions are clearly wrong.There are no obvious logs indicating failure.I’m unsure where to even start debugging.
I enabled autoscaling to handle traffic spikes.Instead of improving performance, latency increased.Cold starts seem frequent.This feels counterproductive.
Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
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.
Feature distributions look stable.But prediction quality is declining.Simple drift metrics don’t explain it.Something deeper seems wrong.