retraining

"It depends" is correct and unhelpful. The decay-curve method that replaces guesswork with measurement, trigger-based vs scheduled retraining, and the safety rails without which auto-retraining is an outage generator.

Auto-retrain is easy; auto-promote is where teams ship incidents. The champion/challenger gate design, two eval sets, segment floors, guardrail metrics, and the defensible line on when a human stays in the loop.

'Retrain weekly' is the answer interviewers let you finish before dismantling it. The four trigger types, how to stop a drift alarm from causing a retraining storm, and why the trigger question is really a cost-and-staleness trade.

The bug that makes offline metrics a lie: training on information that didn't exist at prediction time. A concrete fraud example with timestamps, the as-of join that fixes it, and the label-maturity trap automated retraining adds on top.

The lead-level capstone: not 'which metrics' but 'what's your operating program.' SLOs per model, a drift policy with teeth, retraining as routine rather than rescue, and the inventory discipline that separates teams who run models from teams who babysit them.