Omniforce: on human-centered, large model empowered and cloud-edge collaborative AutoML system

Addressing the open-environment issue with pure data-driven approaches, especially for large models (LM) that require great efforts for data curation and mix, training recipes, and collaboration with small models, makes current Automated machine learning (AutoML) systems inefficient and computationally intractable. We introduce OmniForce (OF), a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques, putting an AutoML system into practice and building AI in open-environment scenarios, such as using large models in industrial supply chains. Concretely, we present OmniForce in terms of management for data, search space, and algorithms; pipeline-driven development and deployment collaborations; and widely provisioned and crowd-sourcing application algorithms. OmniForce can be run either on a public/private cloud or in an on-premise environment, and the models constructed by OmniForce can be quickly and automatically turned into remote services, dubbed model as a service (MaaS). Experimental results obtained demonstrate the efficacy and efficiency of OmniForce.

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Publication Details

Journal
npj Artificial Intelligence
Published
2025-05-14
DOI
https://doi.org/10.1038/s44387-025-00002-0
Citations
4
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
9.92
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article

Omniforce: on human-centered, large model empowered and cloud-edge collaborative AutoML system

Zhenfang Wang, Guanpu Chen, Chang Li, Chaoyue Wang et al.
4 citations
npj Artificial Intelligence
Machine Learning and Data Classification
9.92
article

Omniforce: on human-centered, large model empowered and cloud-edge collaborative AutoML system

Zhenfang Wang, Guanpu Chen, Chang Li, Chaoyue Wang, Jing Zhang, Yiyan Zhao, Li Shen, Shixiang Chen, Bohua Cai, Dongkai Liu, Dacheng Tao, Yibing Zhan, Rongcheng Bian, Yukang Zhang, Xiangyang Liu, Wei Liu, Fei Wang, Liang Ding, Yibo Yang, Fengxiang He, Jiaxing Li, Chao Xue, Qiong Cao, Daqing Liu, Shijin Zhang, Heliang Zheng, Xuyang Peng, Shuai Xie, Yingmei Bai, Shanshan Zhao
article en
4 citations

Abstract

Addressing the open-environment issue with pure data-driven approaches, especially for large models (LM) that require great efforts for data curation and mix, training recipes, and collaboration with small models, makes current Automated machine learning (AutoML) systems inefficient and computationally intractable. We introduce OmniForce (OF), a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques, putting an AutoML system into practice and building AI in open-environment scenarios, such as using large models in industrial supply chains. Concretely, we present OmniForce in terms of management for data, search space, and algorithms; pipeline-driven development and deployment collaborations; and widely provisioned and crowd-sourcing application algorithms. OmniForce can be run either on a public/private cloud or in an on-premise environment, and the models constructed by OmniForce can be quickly and automatically turned into remote services, dubbed model as a service (MaaS). Experimental results obtained demonstrate the efficacy and efficiency of OmniForce.

npj Artificial IntelligenceVol. 1(1)
Jingdong (China) (CN)
Openalex Percentile: Top 2%
Machine Learning and Data Classification
9.92
DOIOpen Access PDFOpenAlex
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