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English(EN) Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

AWS SageMaker HyperPod 增强了 ML 团队的治理和开发工作流

作者 PulseAugur 编辑部 · [2 个来源] ·

AWS 为 Amazon SageMaker HyperPod 推出新功能，增强了机器学习团队的管理和治理能力。这些更新允许更好地管理共享计算集群，包括定义团队访问权限、容量分配和使用策略。此外，用户现在可以直接在 SageMaker Studio 中创建和管理交互式开发环境（称为 SageMaker Spaces），从而简化了从集群访问到高效开发的流程。 AI

影响 通过简化对大规模计算和开发环境的访问来简化 ML 工作流。

排序理由 该集群描述了现有 AWS 产品的新功能和可用性改进，而不是新模型发布或基础研究。

在 AWS Machine Learning Blog 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AWS SageMaker HyperPod 增强了 ML 团队的治理和开发工作流

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该集群描述了现有 AWS 产品的新功能和可用性改进，而不是新模型发布或基础研究。
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2 independent sources
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product, infra
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High
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Breaking (< 6h)
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报道来源 [2]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Geethanjali Banoth ·

    Amazon SageMaker HyperPod 管理和治理的最佳实践

    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…

  2. AWS Machine Learning Blog TIER_1 English(EN) · Giuseppe Angelo Porcelli ·

    直接从 SageMaker Studio 管理 Amazon SageMaker HyperPod Spaces

    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.

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