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.

In this blog post, we’ll demonstrate how to use Cribl Search for anomaly detection by finding statistical outliers in host CPU usage. By monitoring the “CPU Busy” metric, we can identify unusual spikes that may indicate malware penetration or high load/limiting conditions on customer-facing hosts. The best part? This simple but powerful analytic is easily adaptable to other metrics, making it a versatile tool for any data-driven organization.

We have been busy at work under the hood of the Netdata agent to introduce new capabilities that let you extend the "training window" used by Netdata's native anomaly detection capabilities. This blog post will discuss one of these improvements to help you reduce "false positives" by essentially extending the training window by using the new (beautifully named) number of models per dimension configuration parameter.

The past decade has seen organizations embrace AI and data analytics at scale. In 2022, IBM found that 35% of organizations have embraced AI—a 4% increase from 2021. The trend of AI adoption will continue to play out in the next several years across virtually every organizational function. At the vanguard of this movement is AIOps, which sees AI used to improve IT operations (ITOps).

Outlier Detection is now available as part of the Grafana Machine Learning toolkit in Grafana Cloud for Pro and Advanced users. With this feature, you can monitor a group of similar things, such as load-balanced pods in Kubernetes, and get alerted when some of them start behaving differently than their peers. There’s supposed to be a video here, but for some reason there isn’t. Either we entered the id wrong (oops!), or Vimeo is down.

Companies today are relying on technology more than ever thanks to widespread digital transformation and cloud initiatives. And this is increasing the need for safe, efficient and reliable IT environments. But maintaining operational IT stability is very difficult when considering the complex and dynamic nature of today’s IT environments. In fact, IT environments are constantly changing, with new network devices, users and software versions coming into existence.

This article was originally published in The New Stack and is reposted here with permission. Hundreds of billions of sensors produce vast amounts of time series data every day. The sheer volume of data that companies collect makes it challenging to analyze and glean insights. Machine learning drastically accelerates time series data analysis so that companies can understand and act on their time series data to drive significant innovation and improvements.