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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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.
Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
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.