一句话：提出评估ML赋能软件系统在数据集偏移下鲁棒性的方法论，并设计通知机制辅助ML组件调试。
机器学习能力的进步推动其融入软件系统，但软件工程与机器学习实践之间存在明显鸿沟，阻碍智能服务开发。本文关注生产级机器学习系统的鲁棒性，提出一种面向机器学习赋能软件工程系统的方法论，用于评估系统在数据集偏移问题下的鲁棒性属性。该方法将自动化鲁棒性评估，并引入通知机制以辅助机器学习组件的调试，从而弥合软件工程与机器学习实践之间的差距。摘要主要描述研究构想，未给出具体实现细节或实验结果。
The advances in machine learning (ML) have stimulated the integration of their capabilities into software systems. However, there is a tangible gap between software engineering and machine learning practices, that is delaying the progress of intelligent services development. Software organisations are devoting effort to adjust the software engineering processes and practices to facilitate the integration of machine learning models. Machine learning researchers as well are focusing on improving the interpretability of machine learning models to support overall system robustness. Our research focuses on bridging this gap through a methodology that evaluates the robustness of machine learning-enabled software engineering systems. In particular, this methodology will automate the evaluation of the robustness properties of software systems against dataset shift problems in ML. It will also feature a notification mechanism that facilitates the debugging of ML components.
DOI 原文 ·
@inproceedings{paperbot5904,
title = {Towards robust production machine learning systems},
author = {Hala Abdelkader},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3415281}
}