Applying AI/ML Methods to Diagnose Network Issues Using Telemetry Data - Frank Brockners, Cisco#

Frank Brockners, Distinguished Engineer, Cisco (Outshift/CTO division)

• Why: ML/LLM-assisted troubleshooting is the most immediately actionable part of "AI for networking" — this grounds it in real telemetry data and explicitly discusses which techniques fit which phase of diagnosis, which is the question practitioners actually face.
• What: A working examination of which AI/ML approaches (including LLMs) fit problem identification vs. diagnosis phases, demonstrated on real-world network telemetry, with a candid discussion of advantages and drawbacks of each technique. (from abstract only)
• How: Uses real telemetry datasets as the substrate and walks diverse AI/ML methods through the diagnostic workflow end to end rather than presenting a single-point product claim.
• Where applicable: Network operations teams with telemetry pipelines in place; technique-level rather than org-specific. Cisco presenter, but the framing is methodology-first; no quantified accuracy figures in the abstract.