Work out retraining frequency instantly with clear inputs, formula shown and shareable results.
If a model loses accuracy at a roughly steady rate, the retraining interval is the tolerable loss divided by the monthly decay. That converts a vague we should retrain sometimes into a schedule and a budget line. The decay rate has to be measured, not guessed: hold out recent data and evaluate an old model against it to get the slope.
Retraining interval
months = tolerable loss / monthly decay; runs per year = 12 / months; annual cost = runs x cost per run
Triggered retraining is more efficient but needs reliable monitoring and labels. Many teams run both: a scheduled floor plus a trigger for sudden shifts.
Each retrain carries validation, approval and rollout risk, and frequent model churn makes downstream behaviour hard to reason about. There is a real operational cost beyond compute.