AWS Machine Learning Blog· Geethanjali Banoth·· 10 d ago
Best practices for Amazon SageMaker HyperPod administration and governance
Best practices for Amazon SageMaker HyperPod administration and governance
AI summary
Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers.
Selection record
Threshold 60Official, first-handFirst 22Second 22
Not admittedSum of both 44 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：AWS官方博客，详述SageMaker HyperPod机器学习集群治理
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com