canary

This is two questions hiding as one: how would you have caught it before full rollout, and how do you debug it now. Answer both and you signal senior judgment. Here is the staged-rollout and root-cause playbook.

Deploying a model is not flipping a switch. The signal is knowing what each rollout strategy validates, why shadow is uniquely useful for ML, and keeping rollback one step away. Here is the comparison and when to use each.

A bad model in production needs a fast, safe rollback, but ML rollback is trickier than code: the model is data and the truth signal lags. The signal is pre-defined criteria plus a previous version kept warm. Here is the answer.

Shadow deployment validates a new model on real traffic without exposing users to it. The signal is knowing outputs are served in parallel then discarded, and how it complements canary and A/B. Here is the answer.

Manual rollback means minutes of damage while someone wakes up. The signal is defining the trigger signals, thresholds, and guardrails that revert automatically, without flapping on noise. Here is how to make the loop safe.

Saying 'route 5% to the canary' is the easy part. The signal is how you split traffic deterministically, gather enough signal to decide, and ramp on evidence rather than vibes. Here is the mechanics layer.