How do you design a clustering system that supports multi-tenant isolation in production ML platforms?

Multi-tenant clustering systems isolate data, compute, and models per tenant using logical or physical separation.

In enterprise ML platforms, multiple clients (tenants) share infrastructure but require strict isolation. Clustering pipelines must ensure that data from one tenant never influences another. This is achieved through namespace-based partitioning, dedicated feature stores, and tenant-scoped model training. Compute isolation can be enforced via Kubernetes namespaces or separate Spark jobs. Logical isolation ensures correctness, while physical isolation ensures security and performance guarantees.