I am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
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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 ...
Offline metrics improved noticeably.But downstream KPIs dropped.Stakeholders lost confidence.This disconnect is concerning.
I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
