Best practices for Amazon SageMaker HyperPod administration and governance
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This article outlines best practices for administering and governing Amazon SageMaker HyperPod clusters when shared across multiple machine learning teams via Amazon SageMaker Unified Studio. It emphasizes a four-layer control model consisting of organization, project, cluster, and workload boundaries to ensure proper governance. The authors recommend centralizing capacity administration in a dedicated account while using approved cross-account access for consumer teams. Key administrative steps include separating identity from workload permissions, implementing strict network policies, and establishing a formal connection contract for every cluster-project link. To prevent data leakage, the guide advises configuring task-view restrictions so users can only see their own workloads. Furthermore, it highlights the importance of distinguishing between authorization controls and scheduling policies to manage fair-share allocations effectively. Finally, the post stresses using observability metrics as a feedback loop to monitor utilization and adjust policies, ensuring that infrastructure teams retain operational control while ML teams enjoy streamlined access.
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