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https://speakerdeck.com/mski_iksm/shi-lin-chuang-websahisuling-yu-tefalseji-jie-xue-xi-yan-jiu-kai-fa-falsebiao-zhun-hua
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並列して走る機械学習案件をどのように効果的に捌いているか説明。
①タイトな締切 → 高速化で対処 → よく使う機能をML自身に実装する ②並行して走る案件 → 並列化 → Kubernetesを用いて、タスクごとに異なるノードで分散処理(e.g CVのFoldごとにノード分散、推論ユーザごとにノード分散)要件に合わせて、メモリ優先、CPU優先などのノードをノードプールから使い分ける ③属人化 → 標準化 → よく使う機能はMLシステム自身に実装 → 設定ファイルで学習、推論の挙動を制御
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https://speakerdeck.com/mski_iksm/shi-lin-chuang-websahisuling-yu-tefalseji-jie-xue-xi-yan-jiu-kai-fa-falsebiao-zhun-hua
The text was updated successfully, but these errors were encountered: