  This paper proposes the neural publish/subscribe paradigm, a novel approach
to orchestrating AI workflows in large-scale distributed AI systems in the
computing continuum. Traditional centralized broker methodologies are
increasingly struggling with managing the data surge resulting from the
proliferation of 5G systems, connected devices, and ultra-reliable
applications. Moreover, the advent of AI-powered applications, particularly
those leveraging advanced neural network architectures, necessitates a new
approach to orchestrate and schedule AI processes within the computing
continuum. In response, the neural pub/sub paradigm aims to overcome these
limitations by efficiently managing training, fine-tuning and inference
workflows, improving distributed computation, facilitating dynamic resource
allocation, and enhancing system resilience across the computing continuum. We
explore this new paradigm through various design patterns, use cases, and
discuss open research questions for further exploration.
