NetOps teams operating Data Center and WAN networks are challenged by endless noise and reactive processes. Hybrid and multi-cloud networks present additional new difficulties. The infrastructure is mixed, with diverse telemetry formats and sources, and the tooling for detection and troubleshooting is siloed across multiple vendors and infrastructure providers. On top of all this, the pressure is greater than ever to maintain peak availability and performance for critical applications.
In this PoC, Augtera will demonstrate how machine learning can transform NetOps from reactive to proactive. We will show how Augtera’s Network AI platform stops the noise, detects operationally relevant anomalies and automates NetOps processes — dramatically reducing mean-time-to-detection and remediation. We’ll show real world application of topology aware ML to auto-correlate events across mixed telemetry types from data center and hybrid infrastructure, including syslog, SNMP, flow data and more. Augtera’s ML platform will use these data sources to detect multiple operationally relevant failures as they happen, pinpoint the infrastructure components involved, and even predict impending failures before they affect application availability.