Interpretable Feedback for AutoML and a Proposal forDomain-customized AutoML for Networking - Arzani, Behnaz; Hsieh, Kevin; Chen, Haoxian (HotNets 2021, 2021)
The barrier to entry for network operators to use machine learning (ML) is high for operators who are not ML experts. Automated machine learning (AutoML) promises operators the ability to train ML models without requiring the expertise of data scientists or the need to learn ML. However, AutoML today: (a) is black-box; and (b) does not allow operators to leverage domain expertise. We start this paper by describing our broader vision for a domain-customized AutoML platform for networking and propose a set of potential solutions to realize that vision. As the first step, we introduce our feedback solution for AutoML that allows domain experts (who are not experts in ML) to better understand how to improve the input data to AutoML in order to achieve better accuracy.