How do you design a clustering system control plane for managing multiple clustering models in production?

A clustering control plane manages model lifecycle, versioning, deployment, and routing decisions across multiple clustering models.

In large ML platforms, clustering models are not static artifacts but managed services. A control plane is responsible for registering models, versioning them, validating inputs, and routing inference requests to the correct model version. It decouples model management from execution. This enables safe rollouts, A/B testing, rollback, and canary deployments. The control plane also tracks metadata like feature schemas, performance metrics, and drift indicators to automate lifecycle decisions.