I enabled autoscaling to handle traffic spikes.Instead of improving performance, latency increased.Cold starts seem frequent.This feels counterproductive.
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Training loss decreases smoothly.Validation loss fluctuates.Regularization is enabled.Still, generalization is poor.
Feature distributions look stable.But prediction quality is declining.Simple drift metrics don’t explain it.Something deeper seems wrong.
Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
I retrained my model with more recent data.The assumption was that newer data would improve performance.Instead, the new version performs worse in production.This feels counterintuitive and 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.
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 ...
When something fails, tracing the issue takes hours.Logs are scattered across systems.Reproducing failures is painful.Debugging feels reactive.
Predictions affect business decisions.Stakeholders ask “why” a lot.Raw probabilities aren’t helpful.Trust is fragile.
Training data looks correct.Live predictions use the same features by name.Yet values don’t match expectations. This undermines trust in the system?