In this video we will walk though the Netdata Anomaly Advisor deepdive python notebook. The aim of this notebook is to explain, in detail, how the unsupervised anomaly detection in the Netdata agent actually works under the hood. No buzzwords, no magic, no mystery :) Try it for yourself, get started by signing in to Netdata and connecting a node. Once initial models have been trained (usually after the agent has about one hour of data, zero configuration needed), you'll be able to start exploring in the Anomaly Advisor tab of Netdata.

The latest Netdata Agent release v1.36 introduces anomaly rates on every chart, a centralized admin interface, metric correlation improvements, and a new tiering mechanism that allows almost unlimited metrics for your nodes. Want to know more? Hit "Subscribe" and post a comment or ask your question below.

Anomaly rate information is now available embedded in every Netdata chart! Now you are always just a click away from finding out if the metric you are interested in is exhibiting anomalous behavior or not. In this video we walk through some real world usage of the new anomaly rate embedded chart.

The latest Netdata Agent release v1.35 introduces massive improvements for the machine learning-powered Anomaly Advisor, Metric Correlations, Kubernetes monitoring, and much more. Anomaly Advisor & on-device Machine Learning This release features a launch of the flagship machine learning (ML) assisted troubleshooting Anomaly Advisor. Unsupervised ML models are trained for every metric, at the edge, on your devices, enabling real-time anomaly detection across all your systems and applications.

OK, it took our video guy exactly 42 seconds, but we're allowing for some leeway here. Basically, if you want to monitor a Linux node, whether physical, virtual, container, or IoT, all you need to do is.

In this video, our Analytics & ML Lead, Andrew Maguire, walks through an example case study using the K6 load testing platform to run a load test against some of our demo servers running Netdata. Watch in real-time as the Anomaly Advisor reacts to the load test and painlessly surfaces the most anomalous metrics, making it easy to just "see" the load test and how it plays out on the servers.

This video livestream meetup by Netdata takes a deep dive into the fundamentals of Machine Learning in DevOps Infrastructure Monitoring. It also covers the Netdata way of approaching Machine Learning. The Anomaly Advisor major update to Netdata is introduced as a valuable troubleshooting tool for any DevOps or Site Reliability Engineer looking for anomalies in their infrastructure. The hosts share real-world infrastructure monitoring & troubleshooting examples, as well as early feedback from the community on the Anomaly Advisor.

Netdata's Lead Machine Learning Engineer, Andrew Maguire, walks through how to configure the all-new Anomaly Advisor. This new feature lets you troubleshoot in real-time, at scale, by identifying periods of time with raised anomaly rates across your entire infrastructure. In this guided video, Andrew will explain how to enable Netdata's ML functionality then, how to set up unsupervised anomaly detection with minimal configuration, and lastly how the Anomaly Advisor works to speed up troubleshooting when an incident occurs.

Troubleshoot at scale with our all-new, lightweight Anomaly Advisor, powered by machine learning. The Anomaly Advisor finds periods of time with elevated anomaly rates across your entire infrastructure faster than ever before. This new feature works along with our ML unsupervised models on the edge, making your troubleshooting trouble-free! Even better, the Anomaly Advisor requires minimal configuration and is extremely lightweight. No need to worry about exhausting your CPU usage.

Kubernetes monitoring can be confusing, but we're here to help. With Netdata, you can have high-fidelity monitoring of Kubernetes in a process so easy that we can explain it in just two minutes.