Lilly Tech Systems
AI Root Cause Analysis
Learn how artificial intelligence accelerates the identification of root causes behind network incidents, outages, and performance degradation — from correlation mining to automated causal inference.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is AI-driven root cause analysis? Understand how AI transforms incident investigation from hours of manual triage to seconds of automated diagnosis.
2. Correlation Mining
Discover patterns and relationships between network events, metrics, and logs using statistical correlation and association rule mining.
3. Causal Inference
Move beyond correlation to causation using Granger causality, do-calculus, and structural causal models for network diagnostics.
4. Graph Analysis
Model network dependencies as graphs and use graph algorithms and GNNs to trace fault propagation paths to root causes.
5. Automated RCA
Build end-to-end automated RCA systems that combine anomaly detection, causal analysis, and knowledge bases for instant diagnosis.
6. Best Practices
Operational strategies for deploying AI RCA in production, building knowledge bases, and continuous improvement.
What You'll Learn
By the end of this course, you'll be able to:
Mine Correlations
Discover hidden relationships between network events, metrics, and configuration changes that indicate shared root causes.
Infer Causality
Apply causal inference techniques to distinguish true causes from mere correlations in complex network environments.
Trace Dependencies
Use graph-based analysis to model network topology and service dependencies for rapid fault localization.
Automate Diagnosis
Build automated RCA pipelines that diagnose incidents in real time, reducing MTTR from hours to minutes.