What is model serving SLA design for high-scale ML systems?

Model serving SLA design defines measurable reliability, latency, and availability guarantees for ML inference systems.

SLA design in ML systems involves defining strict performance targets such as p50/p95/p99 latency, throughput (QPS), uptime, and error rate budgets. Unlike traditional services, ML SLAs must also consider model quality degradation, drift tolerance, and feature freshness. Designing SLAs requires balancing business objectives with infrastructure constraints. Teams typically define SLOs internally and derive SLAs from them. Enforcement is achieved via monitoring, autoscaling, circuit breakers, and automated rollback systems.