#anomaly-detection

#anomaly-detection

SHAP for feature attribution SHAP quantifies each feature's contribution to a model prediction, enabling: LIME for local interpretability LIME builds simple local models around a prediction to show how small changes influence outcomes. It answers questions like: "Would correcting age change the anomaly score?" "Would adjusting the ZIP code affect classification?" Explainability makes AI-based data remediation acceptable in regulated industries.

Modern telecommunications networks are undergoing a fundamental transformation, evolving from reactive models to proactive, intelligent systems powered by artificial intelligence (AI) that can predict, prevent, and resolve issues before they impact customers. This evolution represents three distinct phases of network intelligence, each building upon the last to create increasingly sophisticated operational capabilities. Detecting Network Anomalies: The Foundation of Smart Operations The first phase focuses on detecting network anomalies through advanced monitoring systems using the current systems' tools and capabilities.

This is where alert fatigue sets in. DevOps and SRE teams working with cloud workloads, microservices, and rapid deployments see hundreds of alerts triggered every day. Many are duplicates, some are irrelevant, and only a handful actually point to issues that demand attention. Result? When every alert screams 'critical', nothing feels urgent. Engineers spend hours triaging false positives, and important signals risk being buried. That delay directly increases mean time to resolution (MTTR), which ultimately means frustrated customers and financial loss for the business.

The feature leverages AI to translate millions of IoT data points into clear, actionable insights for operations and procurement teams, compressing days of analysis and synthesis work down to a matter of seconds. The agentic AI application ensures repeated synthesis of high-volume data to identify patterns and trends across four key areas: asset location, utilization, alerts, and general status updates, as well as sensor readings from monitoring devices.