@inproceedings{liang2020autosys,
author = {Liang, Chieh-Jan Mike and Xue, Hui and Yang, Mao and Zhou, Lidong and Zhu, Lifei and Li, Zhao Lucis and Wang, Zibo and Chen, Qi and Zhang, Quanlu and Liu, Chuanjie and Dai, Wenjun},
title = {AutoSys: The Design and Operation of Learning-Augmented Systems},
organization = {USENIX},
booktitle = {ATC (USENIX Annual Technical Conference)},
year = {2020},
month = {July},
abstract = {Although machine learning (ML) and deep learning (DL) provide new possibilities into optimizing system design and performance, taking advantage of this paradigm shift requires more than implementing existing ML/DL algorithms. This paper reports our years of experience in designing and operating several production learning-augmented systems at Microsoft. AutoSys is a framework that unifies the development process, and it addresses common design considerations including ad-hoc and nondeterministic jobs, learning-induced system failures, and programming extensibility. Furthermore, this paper demonstrates the benefits of adopting AutoSys with measurements from one production system, Web Search. Finally, we share long-term lessons stemmed from unforeseen implications that have surfaced over the years of operating learning-augmented systems.},
publisher = {USENIX},
url = {http://approjects.co.za/?big=en-us/research/publication/autosys-the-design-and-operation-of-learning-augmented-systems/},
}