Настройка порогов и контрольных проверок качества

A quality gate is an automatic rule that stops a bad model from shipping. If the new model misses the bar, the build simply fails.

Choose one metric that matters most for the task, like F1 or AUC. Your primary metric drives the gate so the decision stays clear.

The simplest gate is a hard floor. An absolute threshold blocks any model whose score falls below a fixed minimum value.

Beating a dumb model proves real value. A baseline gate fails the build if you cannot beat predicting the majority class.

New should not be worse than current. A regression gate compares the candidate to the live model and rejects any drop.

Scores wobble run to run from randomness. A small tolerance avoids flaky failures while still catching real regressions.

Overall numbers can hide weak groups. A slice gate requires the worst segment to clear its own minimum, not just the average.

Latency and model size matter in production. Add gates on non-accuracy limits so a slow or huge model also fails the build.

A failing assertion returns a non-zero exit code. CI reads that exit code and stops the deploy, so no one ships by accident.

Log every gate result to MLflow or a file. A recorded history lets you see how thresholds and scores trend over time.

Set the bar where it blocks bad models but not good ones. Revisit each threshold as the model and the business mature.

Why add a small tolerance to a regression gate instead of requiring exact parity?

You now set quality gates: absolute floors, baseline and regression checks, slice and latency limits that fail the build before a weak model ships. ✅