  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.
