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.
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.
“The data center is dead.” It’s certainly a popular opinion. Public cloud revenue is growing at roughly 25% YoY with an estimated $330 billion total revenue for 2021. And Gartner predicted in 2019 that 80% of enterprises would close their traditional data centers by 2025. With stats like those, no one would fault you for thinking that servers are going to be a thing of the past for most enterprises — just like typing pools and interoffice memos.
The use of artificial intelligence and machine learning technologies only makes sense for a company if they profoundly transform the operational experience by improving the company’s KPI performance