Question

You promote a new recommendation model to a 5% canary. Within minutes the canary's error rate climbs to ~12% (control is 0%): some requests return a 500 'invalid score' while others return an empty recommendation list. The canary and control share the same feature store and infra. Dashboards: the failing canary requests all involve users with no purchases in the last 90 days; tracing shows the canary model computes a new feature 'avg_order_value_90d' as total_spend / order_count, and for these users order_count is 0, producing NaN/Inf that propagates into the score. Control (old model) doesn't use this feature. How do you triage and respond?