Real-Time Inference Program Playbook
A four-phase program to deliver low-latency real-time ML inference: latency SLOs, a streaming online feature path, resilience patterns and load testing, and canary rollout with combined SLO and drift monitoring.
Real-Time Inference Program Playbook
Real-time inference serves model predictions synchronously within tight latency budgets, for use cases like fraud scoring, recommendations, and personalization. This playbook delivers a real-time inference capability that meets strict SLOs while keeping features fresh and the system resilient under load.
Phase-by-Phase
Latency Requirements. Define latency SLOs at the percentiles that matter, usually p99. Map feature freshness so the online path serves up-to-date inputs. Size traffic to plan capacity.
Serving Architecture. Design low-latency serving with co-located models and minimal hops. Build an online feature path, often backed by streaming so features stay current. Plan caching for repeated lookups behind an API gateway.
Performance and Resilience. Optimize inference latency through batching, quantization, and warm pools. Add fallbacks and bulkheads so a slow model does not take down the service. Load test to confirm headroom and inject failures with chaos engineering.
Production and Monitoring. Roll out with canary releases. Monitor SLOs and model drift together. Establish on-call with runbooks for latency and feature-staleness incidents.
Team and Roles
An architect designs the serving and feature topology. SREs own SLOs, load testing, and on-call. Data engineers build the online feature path. Backend engineers integrate the inference API.
Risks and Mitigations
- Latency spikes breach SLOs; mitigate with warm pools, caching, and autoscaling headroom.
- Stale features degrade accuracy; mitigate with a streaming online feature path and freshness monitoring.
- Cascading failure from a slow dependency; mitigate with circuit breakers and bulkheads.
Success Criteria
Track p99 latency, SLO attainment, and throughput. Success means consistently meeting latency SLOs at the required throughput.
Tooling
Kubernetes hosts the serving fleet. Redis backs the low-latency feature cache. Kafka feeds the streaming feature path. Prometheus tracks SLOs and Python wraps the models.