The Everything Feed - All Packet Pushers Pods NAN118: The Importance of the Data Behind AI in Networks (Sponsored)
Apr 1, 2026
Joby Rudolph, Senior Distinguished Engineer focused on agent and platform design, and Surya Nimmagadda, Chief Data Scientist specializing in network observability and ML, join to discuss the data behind AI for networks. They talk about agentic tools reshaping workflows. They dig into transparency versus proprietary data, cross-domain observability, customer-specific modeling, noise filtering, and human oversight for AI-driven networking.
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Data Science Plus Network Expertise Makes AI Useful
- Selector began from networking hardware experience and shifted to combine AI/ML with networking subject-matter expertise to bridge silos between data scientists and network teams.
- Surya explained the founding idea: generic ML plus domain context produces useful outcomes rather than treating AI as a mysterious single model.
Agents Changed A Software Engineer's Daily Work
- Joby described how agents transformed his software workflow from writing code to instructing agents what to do, speeding development.
- He shared that in the last year he moved from typing code to directing agents, and that revolution is coming for network engineering.
Transparency Builds Trust Because Data Is The Secret Sauce
- Selector treats openness about architecture as a way to build customer confidence and shows that the secret sauce is high-quality data, not hidden algorithms.
- Surya argues transparency helps validate outcomes and educates customers that data, not opaque models, matters most.