How do you design observability for clustering systems in large-scale ML platforms?

Observability in clustering systems includes metrics, logs, traces, and model-level diagnostics like drift and cluster stability.

Observability is critical for understanding clustering behavior in production. It includes system metrics (latency, throughput), model metrics (inertia, silhouette score), and business metrics (conversion impact). Additionally, cluster drift tracking monitors how centroids evolve over time. Distributed tracing helps track data flow from ingestion to inference. Without observability, clustering systems become opaque and unmanageable.