Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Siqi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu

Abstract

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.

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BibTeX

@inproceedings{10.1145/3399579.3399927,
  author = {Curino, Carlo and Godwal, Neha and Kroth, Brian and Kuryata, Sergiy and Lapinski,   Greg and Liu, Siqi and Oks, Slava and Poppe, Olga and Smiechowski, Adam and Thayer, Ed and   Weimer, Markus and Zhu, Yiwen},
  title = {MLOS: An Infrastructure for Automated Software Performance Engineering},
  year = {2020},
  isbn = {9781450380232},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3399579.3399927},
  doi = {10.1145/3399579.3399927},
  abstract = {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.},
  booktitle = {Proceedings of the Fourth International Workshop on Data Management for   End-to-End Machine Learning},
  articleno = {3},
  numpages = {5},
  location = {Portland, OR, USA},
  series = {DEEM'20}
}