MLOS: An Infrastructure for Automated Software Performance Engineering

被引：5

作者：

Curino, Carlo

Godwal, Neha

Kroth, Brian

Kuryata, Sergiy

Lapinski, Greg

Liu, Siqi

Oks, Slava

Poppe, Olga

Smiechowski, Adam

Thayer, Ed

Weimer, Markus

Zhu, Yiwen

机构：

来源：

PROCEEDINGS OF THE 4TH WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING, DEEM 2020 | 2020年

关键词：

D O I：

10.1145/3399579.3399927

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

MLOS is a Data Science powered infrastructure and methodology to democratize and automate Software Performance Engineering. MLOS enables continuous, instance-level, robust, and trackable systems optimization.

引用

页数：5

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