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English(EN) Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

SageMaker Feature Store 增加了 Lake Formation 和 Iceberg 支持

Amazon SageMaker Feature Store 推出了增强 ML 功能管道的新功能。这些更新包括与 AWS Lake Formation 的原生集成,用于细粒度访问控制,以及新的 Apache Iceberg 表属性,用于管理元数据累积和降低存储成本。这些增强功能可通过 SageMaker Python SDK v3.8.0 获得,旨在简化机器学习操作的功能数据管理和成本可预测性。 AI

影响 提高了 ML 功能管道的效率和成本管理,可能加速生产部署。

排序理由 现有 ML 平台功能的更新。

在 AWS Machine Learning Blog 阅读 →

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SageMaker Feature Store 增加了 Lake Formation 和 Iceberg 支持

报道来源 [2]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Dhaval Shah ·

    Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

    Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance…

  2. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    🤖 Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store Today, we’re announcing three new capabilities available in SageMaker

    🤖 Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For comp…