retraining

The naive answer is 'retrain weekly.' The senior answer is a hybrid: drift-triggered retraining with a max-staleness fallback, plus a gate that a fresh model must beat the incumbent. Here is how to reason about it.

Retrain too often and you burn money and risk regressions; too rarely and the model rots. The strong answer is a layered trigger policy with guardrails, not a single cron job. Here is how to set the cadence.

GDPR erasure and copyright takedowns demand removing a user's influence from trained weights, not just a dataset row. The signal is knowing why deletion isn't enough and where retrain, SISA, and approximate scrubbing each land on the cost-versus-proof curve.