gradual rollout

**Gradual Rollout** Gradual rollout (also called canary deployment or progressive delivery) incrementally increases traffic to a new model or system version—1%, 5%, 10%, 25%, 50%, 100%—monitoring metrics at each stage to detect issues before full deployment, minimizing risk of widespread failures. Rollout stages: (1) canary (1-5% traffic to new version, 95-99% to stable version), (2) early rollout (10-25%), (3) majority rollout (50-75%), (4) full rollout (100%). At each stage, monitor for X hours/days before proceeding. Metrics to monitor: (1) error rate (5xx errors, exceptions, crashes), (2) latency (p50, p95, p99 response times), (3) quality metrics (task-specific—accuracy, BLEU, user satisfaction), (4) resource usage (CPU, memory, GPU utilization), (5) business metrics (conversion rate, engagement). Rollback triggers: (1) error rate increase >X% (e.g., >5% relative increase), (2) latency degradation >Y% (e.g., p95 >20% slower), (3) quality regression (accuracy drop, user complaints), (4) resource exhaustion (OOM, throttling). Rollback procedure: immediately route all traffic back to stable version, investigate root cause, fix issue, restart gradual rollout. Implementation: (1) load balancer routing (weighted routing rules), (2) feature flags (control which users see new version), (3) A/B testing framework (random assignment to versions), (4) traffic splitting (percentage-based routing). Advanced strategies: (1) user-based rollout (internal users → beta users → all users), (2) region-based rollout (one datacenter at a time), (3) time-based rollout (off-peak hours first), (4) cohort-based (specific user segments). Benefits: (1) risk mitigation (limit blast radius of bugs), (2) early detection (catch issues with small user impact), (3) performance validation (real-world traffic patterns), (4) confidence building (gradual validation reduces anxiety). ML-specific considerations: (1) model quality (A/B test new vs. old model), (2) data drift (monitor input distribution changes), (3) feedback loops (new model may change user behavior), (4) cache invalidation (ensure new model predictions used). Gradual rollout is industry best practice for deploying ML models and services, balancing innovation speed with reliability.