Batch predictions look reasonable.Real-time predictions don’t.Same model, same features—supposedly. Yet results differ?
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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.
I enabled autoscaling to handle traffic spikes.Instead of improving performance, latency increased.Cold starts seem frequent.This feels counterproductive.
Traffic is stable.Model architecture hasn’t changed.Yet costs keep rising month over month.It’s hard to explain.
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?
My model works well during training and validation.But inference results differ even with similar inputs.There’s no obvious bug in the code.It feels like something subtle is off.
Offline metrics improved noticeably.But downstream KPIs dropped.Stakeholders lost confidence.This disconnect is concerning.
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.