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.
Nothing changed in the code logic.Only the ML framework version was upgraded.Yet predictions shifted slightly.This caused unexpected regressions?
Overall metrics look acceptable.But certain users receive poor predictions.The issue isn’t uniform. It’s hard to detect early?
The Docker container runs fine on my machine.CI builds succeed without errors.But once deployed, inference fails unexpectedly.Logs aren’t very helpful either.
Batch predictions look reasonable.Real-time predictions don’t.Same model, same features—supposedly. Yet results differ?
An old model is still running in production.Traffic has shifted to newer versions.I want to remove it safely.But I’m worried about hidden dependencies.
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 ...
Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
Training data looks correct.Live predictions use the same features by name.Yet values don’t match expectations. This undermines trust in the system?