What is multi-stage inference architecture in large-scale ML systems?

Multi-stage inference pipelines process inputs through sequential models of increasing complexity.

Multi-stage inference architectures use cascaded models where lightweight models filter easy cases and complex models handle harder ones. This reduces cost and improves latency. Each stage acts as a gatekeeper, reducing load on expensive deep models. This architecture is common in search ranking and recommendation systems.