The AI Behind Networking by Surya Nimmagadda

Selector’s Chief Data Scientist, Surya Nimmagadda, opened by asking the question many customers wonder: “Where’s the AI in Selector AI?” The discussion explored how real-world AI in networking isn’t about magic models—it’s about blending human expertise with machine intelligence to achieve reliable, explainable outcomes. Surya walked through how Selector builds this balance: scalable data ingestion, contextual data enrichment, self-supervised learning for metrics and logs, unsupervised clustering for log patterns, and graph-based correlation and causation to reveal true root causes. Finally, he explained how LLMs and MCP layer on top—not to replace human reasoning—but to summarize insights in natural language and automate trusted workflows safely.

What You’ll Learn:
Human + AI Partnership: Why operational AI must combine machine intelligence with human wisdom and validation.
Data-Centric AI: How Selector prioritizes clean, contextual data over giant models to drive accurate, explainable outcomes.
Multi-Model Architecture: How metrics, logs, correlations, and causation models interact to identify root causes and predict impact.
MCP + LLM Integration: How natural-language interfaces and governed workflows make AI accessible and safe for network engineers.

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