Telecom Network Anomaly Detection and Outage Prediction Suite

Added Jun 2025 3 design docs

Telecom operators run networks where minutes of downtime translate into thousands of frustrated customers and costly SLA penalties, yet operations centers still spend much of their time reacting to outages after users report them. The competitive edge lies in detecting anomalies early and predicting failures before they cascade across the network. The intern develops a network analytics suite where agentic AI monitors performance continuously. A Flask backend in Python ingests real-time network telemetry, including latency, packet loss, and per-node throughput, into MongoDB, and AI agents analyze the streams to detect anomalies, correlate symptoms across the topology, and predict likely outages together with recommended interventions. Interactive dashboards give network operators live views and drill-downs, automated alerting pushes prioritized incidents to the right teams, and Docker containerizes the services for scalable deployment. Secure user management controls who can see and act on each network segment. The intern practices building AI-driven operations tooling: streaming telemetry design, agent workflows that explain their findings, and the alerting discipline that separates useful monitoring from noise.

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