How do you design a self-healing clustering system in production ML platforms?

A self-healing clustering system detects degradation, triggers retraining, and automatically rolls back or adjusts models without human intervention.

Self-healing clustering systems are built using continuous monitoring loops that track cluster quality metrics (silhouette score, inertia drift, centroid stability). When degradation is detected, an automated controller triggers actions such as retraining, reinitialization, or rollback to a previous stable model. These systems rely on closed-loop feedback architectures similar to control systems in engineering. The key is separating detection, decision, and execution layers to avoid cascading failures.