Phillip Gervasi presents at Microsoft Ignite 2025 on how AI innovations, including ML, NLP, and LLMs, are transforming network operations through intelligent, data-driven workflows on Azure.

How AI Transforms Network Operations: From Theory to Reality

Speaker: Phillip Gervasi
Event: Microsoft Ignite 2025
Session ID: ODSP1420

Introduction

AI is accelerating the evolution of network operations (NetOps), moving organizations past legacy scripted automation to intelligent, data-driven workflows. This session explores practical applications and the architecture needed to support modern, AI-powered NetOps.

Key Topics Covered

  • Incident Triage: AI enables faster, more accurate triage by analyzing network data in real-time, identifying anomalies, and prioritizing issues.
  • Knowledge Retrieval: Leverages LLMs and NLP to search vast data repositories and documentation, reducing time to resolution.
  • Traffic Analysis and Prediction: Machine learning models detect traffic patterns, forecast potential issues, and optimize resource allocation.
  • Monitoring Evolution: Contrasts legacy monitoring systems with ML/NLP/LLM-powered analytics. New approaches support predictive and prescriptive capabilities.

AI Architecture for NetOps

  • Solid Data Foundation: Reliability begins with foundational data architecture that supports scalable, real-time workflows.
  • APIs and Integrations: Accurate and timely data access through robust APIs is essential for automated and manual interventions.
  • Natural Language Interfaces: Examples include converting user queries to SQL using NLP, allowing non-technical users to extract insights.
  • Retrieval-Augmented Generation (RAG): RAG integrates external or hybrid network data for enhanced query results and knowledge graphs.
  • Agent Workflows: Multi-agent systems collaborate for complex tasks and manage real-time insights across hybrid environments.

MLOps and Data Pipelines

  • Data Pipelines: Architecting scalable data ingestion, ETL, and monitoring processes enables robust ML model deployment in NetOps.
  • MLOps: Strategies for model training, validation, deployment, and monitoring tailored to dynamic network environments.

Practical Considerations

  • Build vs Buy: Weighing the flexibility of custom solutions vs. the speed and reliability of vendor offerings when implementing AI infrastructure.
  • Accuracy and Reliability: Addressing model performance, trustworthiness, and operational impact in high-stakes network contexts.
  • Cost and Compliance: Managing operational expenses and meeting regulatory requirements (e.g., data privacy).
  • Human-in-the-Loop Guardrails: Ensuring expert oversight for critical automation scenarios, balancing speed and control.

Additional Resources

Conclusion

AI, ML, and NLP are driving a paradigm shift in network operations. By designing intelligent data flows, deploying agentic automation, and integrating human oversight, organizations can achieve real-time insights, reliability, and compliance in modern hybrid networks.