How Netdata's ML-based Anomaly Detection Works
How does Netdata's machine learning (ML) based anomaly detection actually work? Read on to find out!
How does Netdata's machine learning (ML) based anomaly detection actually work? Read on to find out!
Introducing new capabilities expanding hybrid cloud support for VMs, Kubernetes and Linux apps running in public or private clouds, enhancements in application to infrastructure correlation using AI/ML-powered anomaly detection and more.
Detecting unauthorized usage and malicious applications in an instance involves analyzing OS and application logs. Doing this manually is a herculean effort because of the number of logs and the patterns one has to look for. Having a tool that can provide an aggregated view of your instance and the ability to analyze them easily can greatly reduce manual effort.
The menu (on the overview or single node tab) now has an anomaly rate button built into it that, for the entire visible window or a highlighted time range, shows the maximum chart anomaly rate within each section. Read on to learn more about this new feature!
The world of software is growing more complex, and simultaneously changing faster than ever before. The simple monolithic applications of recent memory are being replaced by horizontal cloud-native applications. It is no surprise that such applications are more complex and can break into infinitely more ways (and ever new ways). They also generate a lot more data to keep track of. The pressure to move fast means software release cycles have shrunk drastically from months to hours, with constant change being the new normal.
We have recently extended the native machine learning (ML) based anomaly detection capabilities of Netdata to support all metrics, regardless on their collection frequency (update every). Previously only metrics collected every second were supported, but now Netdata can run anomaly detection out of the box with zero config on metrics with any collection frequency.