Feature distributions look stable.But prediction quality is declining.Simple drift metrics don’t explain it.Something deeper seems wrong.
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A new column was added to the input data.No one thought it would affect the model.Suddenly, inference started failing or producing nonsense results.This keeps happening as systems evolve.
Predictions affect business decisions.Stakeholders ask “why” a lot.Raw probabilities aren’t helpful.Trust is fragile.
The batch prediction job used to run in minutes.As data volume increased, runtime started doubling unexpectedly.Nothing changed in the model code itself.Now it’s becoming a bottleneck in the pipeline.
The same pipeline sometimes succeeds.Other times it fails mysteriously.No code changes occurred.This unpredictability is frustrating.
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?
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.
Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
Batch predictions look reasonable.Real-time predictions don’t.Same model, same features—supposedly. Yet results differ?