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  • An overview and solution for democratizing AI workflows at the network edge [Elektronski vir]
    Čop, Andrej, računalničar ; Bertalanič, Blaž ; Fortuna, Carolina
    With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network ... operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applications and systems at the network edge. While AI models and services are well-managed in cloud systems, achieving similar maturity for serving network needs remains an open challenge. Existing open solutions are emerging and are yet to consider democratization requirements. In this work, we identify key requirements for democratization and propose NAOMI, a solution for democratizing AI/ML workflows at the network edge designed based on those requirements. Guided by the functionality and overlap analysis of the O-RAN AI/ML workflow architecture and MLOps systems, coupled with the survey of open-source AI/ML tools, we develop a modular, scalable, and distributed hardware architecture-independent solution. NAOMI leverages state-of-the-art open-source tools and can be deployed on distributed clusters of heterogeneous devices. The results show that NAOMI performs up to 40% better in deployment time and up to 73% faster in AI/ML workflow execution for larger datasets compared to AI/ML Framework, a representative open network access solution, while performing inference and utilizing resources on par with its counterpart.
    Source: Journal of network and computer applications. - ISSN 1095-8592 (Vol. 239, [article no.] 104180, Jul. 2025, str. 1-23)
    Type of material - e-article ; adult, serious
    Publish date - 2025
    Language - english
    COBISS.SI-ID - 232497667

    Link(s):

    https://www.sciencedirect.com/science/article/pii/S1084804525000773?via%3Dihub
    DOI

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