Requirements:
- Functional: low-latency model inference for global users, user affinity for personalization (serve model + user profile near user), seamless failover during region outage.
- Non-functional: high availability (99.99%), degraded but safe behavior on region failure, eventual consistency for profiles acceptable, strong safety for critical business rules.
High-level architecture:
Edge LB / Global DNS (geo-aware, health-checked) → Regional API Gateways → Regional Model Serving Cluster (K8s + KFServing/MLRun) + Regional Feature Store + Local User Profile Cache → Central Control Plane + Async Replication Fabric (Kafka/CDC) → Model Registry & CI/CD
Core components and responsibilities:
- Regional Serving: containerized model replicas (canary, stable), local feature store/cache for low latency, policy layer for business rules.
- Global Control Plane: model registry, deployment orchestration, global config, cross-region health monitoring.
- Replication Fabric: change-data-capture + Kafka topics for user profiles, features, metrics; async cross-region replication with conflict resolution.
- Global Router: geo-DNS with weighted failover; sticky affinity via signed cookie or token TTL.
State synchronization & consistency:
- Use local writes to regional profile store, ship events via CDC to global Kafka. Prefer eventual consistency for personalization; include vector-clock or last-writer-wins with merge hooks for conflicts. For critical counters, use CRDTs or strongly-consistent central store (sparingly).
Deployment strategy & rollout:
- Blue/green or canary per region orchestrated by control plane. Deploy model version to one region, run A/B, promote progressively. Use feature flags to toggle behavior per region.
- Ensure model artifacts in registry with semantic versioning and migration metadata.
Graceful degradation during region failure:
- Global DNS fails over traffic to next nearest healthy region. Regional serving in failover mode:
- Serve using best-effort local cached features; if missing, fall back to lightweight generic model or rule-based logic to avoid incorrect personalization.
- Mark user as degraded and continue async reconciliation when primary region returns.
Rollback & model divergence handling:
- Implement two-tier rollback:
- Fast rollback: control plane flips traffic or feature flag to previous stable model globally or per-region.
- Data-aware rollback: if models differ materially (schema or output distribution), run compatibility and shadow testing before switch. Maintain backward-compatible scoring adapters.
- Keep deterministic model signatures and input/output contracts; if divergence detected (metric drift, increased error), auto-trigger rollback and alert.
Observability & safety:
- Collect per-region inference metrics, input distributions, feature drift, latencies. Use cross-region alerting and automated SLO-based scaling.
- Run periodic cross-region shadowing: route a small percentage of requests to secondary regions to validate parity.
Trade-offs:
- Latency vs consistency: prioritize local low-latency personalized serving with eventual consistency; use strong consistency only where correctness requires.
- Complexity vs resilience: asynchronous replication and conflict resolution add complexity but enable high availability.
This design provides low-latency personalization with user affinity, safe graceful degradation, clear deployment/rollback paths, and monitored consistency trade-offs suitable for production ML serving across regions.
