Deployment✓ Mathematical
◆ The PatternShip models safely with progressive traffic splitting
Deploying a new model to 100% of traffic is reckless. Canary rollouts send a small fraction (1–5%) of traffic to the new model while monitoring metrics. Blue-green deploys keep the old version warm for instant rollback. A/B tests run both models simultaneously to measure real-world lift.
// Interactive — canary traffic split
Canary %5%
# Kubernetes canary with Istio traffic split # VirtualService: # http: # - route: # - destination: # host: model-service # subset: stable # weight: 95 # - destination: # host: model-service # subset: canary # weight: 5
Rollback triggers: Define automatic rollback criteria before deploying — e.g., if p99 latency exceeds 200ms or error rate exceeds 1%, revert immediately.
Pattern bridge: Canary rollouts apply the same risk management as position sizing — start small, scale up only when evidence confirms safety. The Bayesian A/B framework from The Toolkit directly applies to evaluating canary metrics.