ML Microservices Architecture - Interactive Visualization

A production ML microservices architecture separates concerns across specialized services: the API gateway handles rate limiting and routing; the feature service performs online feature lookup from Redis; the model service runs inference (often with two versions for canary deployments); post-processing applies thresholds and calibration; and the logging service asynchronously records predictions for monitoring and retraining. When a service fails, circuit breakers prevent cascade failures by fast-failing and returning fallback responses instead of waiting for timeouts. Service discovery (Kubernetes DNS or Consul) allows services to find each other without hardcoded addresses.

• Circuit breaker states: closed (normal), open (fast-fail on failure), half-open (probe for recovery with one test request)
• Canary deployment: route 5% of traffic to model-v2 while keeping 95% on stable model-v1 - observe metrics before full rollout
• Async logging: prediction logs are written asynchronously so latency of the logging service does not block the main request path
• Service mesh (Istio): provides distributed tracing, mTLS between services, and traffic management without code changes

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