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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I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
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.
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.
The same pipeline sometimes succeeds.Other times it fails mysteriously.No code changes occurred.This unpredictability is frustrating.
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.
When something fails, tracing the issue takes hours.Logs are scattered across systems.Reproducing failures is painful.Debugging feels reactive.
Nothing changed in the code logic.Only the ML framework version was upgraded.Yet predictions shifted slightly.This caused unexpected regressions?