Best practices for Amazon SageMaker HyperPod administration and governance
Amazon SageMaker HyperPod gives machine learning (ML) teams access to large pools of accelerated compute for training and fine-tuning models.
Key points
- Amazon SageMaker Unified Studio adds another consideration: You can connect a SageMaker HyperPod cluster to a project so team members can launch workloads from their project workspace.
- In this post, we show how to administer SageMaker HyperPod through SageMaker Unified Studio while preserving the underlying governance controls.
- Amazon SageMaker HyperPod is a capability of Amazon SageMaker AI.
- Amazon SageMaker Unified Studio is the data and AI development environment where teams build with their data and tools.
Sources (1)
- [1]Best practices for Amazon SageMaker HyperPod administration and governanceAWS Machine Learning Blog · Oct 6, 03:50 PM
Amazon SageMaker HyperPod gives machine learning (ML) teams access to large pools of accelerated compute for training and fine-tuning models.
Amazon SageMaker Unified Studio adds another consideration: You can connect a SageMaker HyperPod cluster to a project so team members can launch workloads from their project workspace.
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