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KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics #529

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hurutoriya opened this Issue Nov 28, 2017 · 0 comments

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hurutoriya commented Nov 28, 2017

一言でいうと

  • Scalaで書かれた機械学習の大規模分散下における学習のパイプラインを提供するフレームワーク
  • KeystoneMLを用いることで、高精度が保証されスケーラブルな学習ができることをいくつかの領域で示した
  • 従来の分散学習フレームワークのVowpal Wabbit、SystemMLに勝利

Apache Sparkにて動く。

論文リンク

https://arxiv.org/abs/1610.09451

著者/所属機関

Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael Franklin, Benjamin Recht
AMPLab, University of California, Berkeley

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