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
收藏
页数：5
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