Data Center Network AIOps Solution makes core technology usable within a compelling customer context.
Observability Reflections from Monitorama included how much data collection is enough, automation for detection, & log processing challenges.
Compared to threshold-based anomaly detection, machine learning anomaly detection reduces noise and enables proactive action.
The old saying goes “If a tree falls in a forest and no one is around to hear it, does it make a sound?”. Monitoring and observability are similar in nature. So many alerts flow across operations screens, anomalies that require attention roll off quickly, never to be seen, and never to be acted on until a customer or application team calls requesting assistance.
It was a typical SKO affair. Regional reports. Marketing directions. Lead generation, Product roadmaps, Solutions etc. All-day meetings that are necessary and stimulating, but also deserving of a reward for ploughing through and doing the hard work.
Operations teams will sleep better, knowing they will be aware of new log messages and patterns, as soon as they occur, instead of finding out weeks later, after numerous failures have occurred.
As a marketer, I’m thrilled for the opportunity to tell great stories about exceptional products & services that are making a significant difference.
Network Operators: Time to get AI/ML out of the labs and into production
A topology-aware network model is a critical building block for Network AIOps
Data center architectures and operational realities have changed dramatically. However, NetOps tools have not kept pace.