AWS Machine Learning Blog· Giuseppe Angelo Porcelli·· 10 d ago
Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio
Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio
AI summary
Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.
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 SageMaker AI/ML开发环境管理教程
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