Over the last few years we have slowly and methodically been building out the ML based capabilities of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting.
Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a simple node level anomaly rate alert.
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.
We are always trying to lower the barrier to entry when it comes to monitoring and observability and one place we have consistently witnessed some pain from users is around adopting and approaching configuration management tools and practices as your infrastructure grows and becomes more complex.
To that end, we have begun recently publishing our own little example ansible project used to maintain and manage the servers used in our public Machine Learning Demo room.
This post introduces this project as a somewhat simple example of using Ansible with Netdata. Read on to learn more, but more importantly feel free to explore the repo and see how it all hangs together.
How does Netdata's machine learning (ML) based anomaly detection actually work? Read on to find out!
Unlocking the full potential of monitoring through ML integration, anomaly detection, and innovative scoring engines.
Introduction to Netdata's new visualisation providing AI Insights, supporting Rapid Diagnostics.
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!
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.
Following on from the recent launch of our Anomaly Advisor feature, and in keeping with our approach to machine learning, here is a detailed Python notebook outlining exactly how the machine learning powering the Anomaly Advisor actually works under the hood.
As of v1.35.0 the Netdata Agent can now run Metric Correlations (MC) itself. This means that, for nodes with MC enabled, the Metric Correlations feature just got a whole lot faster!
Today we are excited to launch one of our flagship ML assisted troubleshooting features in Netdata – the Anomaly Advisor.
The Anomaly Advisor builds on earlier work to introduce unsupervised anomaly detection capabilities into the Netdata Agent from v1.32.0 onwards.
There is a lot of buzz in the world of machine learning (ML) and as a layperson it can be hard to keep up with it all. Therefore, we decided to write down some of our thoughts and musings on how we are approaching ML at Netdata.
