Workshops Archive

Making the Self-Driving “Net” Work: Developing Production-Ready ML Models for Self-Driving Networks

Workshop: The 12th Annual International Workshop on Innovating the Network for Data-Intensive Science (INDIS)

Authors: Arpit Gupta (University of California, Santa Barbara)

Abstract: The vision of self-driving networks (AIOps) hinges on the ability to develop production-ready machine learning models—models that are not only performant but also generalizable, robust, and trustworthy. Yet, most ML artifacts in networking today remain underspecified, suffering from shortcut learning, spurious correlations, and out-of-distribution failures rooted in data deficiencies. This talk traces our journey toward addressing these challenges by closing the loop between model analysis and data generation. I will present a closed-loop ML pipeline—composed of Trustee for model analysis and NetUnicorn, NetReplica, and NetGent for programmable data generation—that iteratively fixes underspecification by generating "better" data. Building on this foundation, I will discuss our efforts toward developing network foundation models (NFMs) that leverage self-supervised learning on large-scale network telemetry to unify diverse tasks, and toward reasoning about the generalizability of these NFMs. Finally, I will highlight emerging opportunities for using these programmable substrates to reimagine network operations and network measurements—solving unexplored learning problems in networking and revisiting previously explored ones with a fresher perspective.

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