Best Practices for SageMaker HyperPod Cluster Governance

Amazon SageMaker HyperPod lets ML teams share large pools of accelerated compute, but governance is the hard part. This guide outlines four control layers: organization, project, cluster, and workload. SageMaker Unified Studio lets teams launch workloads from project workspaces while infrastructure teams retain cluster control through SageMaker AI interfaces and APIs. Key recommendations include centralizing cluster ownership in a single capacity account, using cross-account access rather than independent capacity administrators, and aligning IAM, EKS, Slurm, and scheduler controls before making a cluster available to project members.