We have recently added a more detailed anomaly rate chart to Netdata that breaks out the overall node anomaly rate by type, this lets you more easily see what parts of your infrastructure might be experiencing an uptick in anomalies when you see the overall node anomaly rate increase.

Hey there, data detectives and server sleuths! Ever find yourself staring at a screen full of numbers and graphs, only to have one data point wave at you like a tourist lost in Times Square? Yup, you’ve stumbled upon the cheeky world of Anomaly Detection—where data points act more mysterious than your cat when it suddenly decides to sprint around the house at 2 AM. So buckle up!

Anomaly detection is one of the most common problems that Splunk users are interested in solving via machine learning. This is highly intuitive, as one of the main reasons our Splunk customers are ingesting, indexing, and searching their systems’ logs and metrics is to find problems in their systems, either before, during, or after the problem takes place. In particular, one of the types of anomaly detection that our customers are interested in is time series anomaly detection.

Anomaly detection in metrics or time series data is the most used machine learning use case among Splunk Security and Observability customers. Customers are looking for easy-to-use ML-powered high-fidelity anomaly detection, so that they can be alerted at the first sign of a failure point or security incident.

“Time is money” couldn’t be truer than in managing cloud costs. By way of proactive anomaly detection, a chance is given to save time that could have been spent on issue recognition and resolution. Anomaly detection for the Cloud can be tricky since there can be changes in prices & data on billing history anytime. Not to mention, seasonality can mess things up as well.

Graphite is used by many organizations to track and visualize various metrics that their applications or servers send out. But what happens if there are too many of these metrics or the company doesn't want to use its human resources to monitor the behaviour of metrics constantly? In this article, we will use Hosted Graphite by MetricFire to learn about Graphite's ability to notify users about the abnormal behaviour of services or infrastructure in a timely manner.

In the complex and dynamic realm of data analytics, real-time anomalies serve as insights to issues a business faces. A pervasive and enduring conundrum persists: accurately discerning between anomalies of significant importance and those of lesser consequence. This distinction is a nontrivial task as not all anomalies bear the same weight.

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.

How does Netdata's machine learning (ML) based anomaly detection actually work? Read on to find out!