Best practices for Amazon SageMaker HyperPod administration and governance

This article provides best practices for administering Amazon SageMaker HyperPod through SageMaker Unified Studio while preserving governance controls across multiple teams and clusters.

• Implement four-layer governance model: organization, project, cluster, and workload controls
• Centralize cluster ownership and capacity administration in a designated capacity account
• Separate administrative and workload identities; preserve distinction between cluster administrators and data scientists
• Use namespace-per-tenant with RBAC, EKS Pod Identity, and default-deny network policies for isolation
• Create connection contracts documenting business owner, cluster details, approved workload types, and scheduling policy
• Restrict task visibility by default; separate read-only access from permissions to create or delete workloads
• Keep authorization (RBAC/ACLs) separate from scheduling policy (task governance/Slurm controls)
• Use observability as governance feedback loop; define owner and response for every monitored signal
• Set review schedules for connections; define event-driven reviews for ownership, roles, or capacity changes

Implement controls in layers, starting with one non-production cluster and test project before scaling to multiple teams.