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!
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
Using Elastic Anomaly detection and log categorization for root cause analysis
Elastic's machine learning helps support several easy-to-use features to help determine root cause analysis for logs. This includes anomaly detection and log categorization, which are easy-to-use features aiding in analysis without the need to understand or know about machine learning.
Managing business objectives with Elastic Observability Anomaly detection
Monitoring business revenues and managing change in revenue with Elastic Machine Learning anomaly detection.
Anomaly Detection Using OSquery and Grafana
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.
Anomaly Rates in the Menu!
In this video we walk through an example of how to use the anomaly rate button within the Netdata menu.
Anomaly Rates in the Menu!
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!
Sponsored Post
Machine-Learning Automation: Processing, Storing, & Analyzing Data in the Digital Age
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.
Anomaly detection on Prometheus metrics
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.
Using Cribl Search for Anomaly Detection: Finding Statistical Outliers in Host CPU Busy Percentage
In this video, 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.
Using Cribl Search for Anomaly Detection: Finding Statistical Outliers in Host CPU Busy Percentage
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.