[2203.15784] Implementation of an Automated Learning System for Non-experts
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Computer Science > Human-Computer Interaction

arXiv:2203.15784 (cs)
[Submitted on 26 Mar 2022]

Title:Implementation of an Automated Learning System for Non-experts

Authors:Phoenix X. Huang, Zhiwei Zhao, Chao Liu, Jingyi Liu, Wenze Hu, Xiaoyu Wang
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Abstract:Automated machine learning systems for non-experts could be critical for industries to adopt artificial intelligence to their own applications. This paper detailed the engineering system implementation of an automated machine learning system called YMIR, which completely relies on graphical interface to interact with users. After importing training/validation data into the system, a user without AI knowledge can label the data, train models, perform data mining and evaluation by simply clicking buttons. The paper described: 1) Open implementation of model training and inference through docker containers. 2) Implementation of task and resource management. 3) Integration of Labeling software. 4) Implementation of HCI (Human Computer Interaction) with a rebuilt collaborative development paradigm. We also provide subsequent case study on training models with the system. We hope this paper can facilitate the prosperity of our automated machine learning community from industry application perspective. The code of the system has already been released to GitHub (this https URL).
Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2203.15784 [cs.HC]
  (or arXiv:2203.15784v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2203.15784

Submission history

From: Wenze Hu [view email]
[v1] Sat, 26 Mar 2022 00:28:29 UTC (2,731 KB)
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