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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Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.
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 ...
I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
Training data looks correct.Live predictions use the same features by name.Yet values don’t match expectations. This undermines trust in the system?