Traffic is stable.Model architecture hasn’t changed.Yet costs keep rising month over month.It’s hard to explain.
Decode Trail Latest Questions
My production data is unlabeled.I can’t calculate accuracy or precision anymore.Still, I need to know if the model is degrading.What can I realistically monitor?
Training data looks correct.Live predictions use the same features by name.Yet values don’t match expectations. This undermines trust in the system?
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.
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.
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.
Training loss decreases smoothly.Validation loss fluctuates.Regularization is enabled.Still, generalization is poor.
I have a new model ready to deploy.I’m confident in offline metrics, but production risk worries me.A full replacement feels dangerous. What’s the safest approach?
Overall metrics look acceptable.But certain users receive poor predictions.The issue isn’t uniform. It’s hard to detect early?
Predictions affect business decisions.Stakeholders ask “why” a lot.Raw probabilities aren’t helpful.Trust is fragile.