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When something fails, tracing the issue takes hours.Logs are scattered across systems.Reproducing failures is painful.Debugging feels reactive.
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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.
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.
Nothing changed in the code logic.Only the ML framework version was upgraded.Yet predictions shifted slightly.This caused unexpected regressions?
The same pipeline sometimes succeeds.Other times it fails mysteriously.No code changes occurred.This unpredictability is frustrating.