performance drop
Decode Trail Latest Questions
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.
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?
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.
Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
I enabled autoscaling to handle traffic spikes.Instead of improving performance, latency increased.Cold starts seem frequent.This feels counterproductive.