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.
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Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.
Different teams trained models independently.Each performs well in certain cases.Now deployment is messy.Choosing one feels arbitrary.
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.
When something fails, tracing the issue takes hours.Logs are scattered across systems.Reproducing failures is painful.Debugging feels reactive.
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.
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.
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?
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.