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English(EN) Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

AWS SageMaker SDK v3 简化边缘计算机视觉模型管理

作者 PulseAugur 编辑部 · [1 个来源] ·

Amazon Web Services 已将其 SageMaker SDK 更新至 3.0 版本，引入了一种在边缘管理计算机视觉模型的新方法。此次更新用 SageMaker Neo 取代了旧的 SageMaker Edge Manager 进行优化，能够无需手动干预即可训练、评估和部署数千个独立的摄像头模型。该系统利用双分支 SageMaker Pipeline 来训练异常检测器和缺陷分类器，并设有质量门来过滤掉表现不佳的模型。此外，AWS Step Functions 协调全舰队训练过程，确保将优化后的模型构件部署到边缘设备。 AI

影响 简化了边缘设备的计算机视觉模型的部署和管理，可能加速其在工业和物联网应用中的普及。

排序理由 对现有用于管理机器学习模型的 SDK 的更新。

在 AWS Machine Learning Blog 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AWS SageMaker SDK v3 简化边缘计算机视觉模型管理

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对现有用于管理机器学习模型的 SDK 的更新。
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报道来源 [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Kiowa Jackson ·

    Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

    Fleet-wide computer vision needs a model per camera, not one model for the whole fleet. This post presents a working Amazon SageMaker SDK v3 reference architecture that trains, quality-gates, compiles per-camera models with SageMaker Neo, and versions thousands of them, with an A…

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