From c4941b49274d82c2d9ee535a7fdaf932da2ebbf7 Mon Sep 17 00:00:00 2001 From: Joel Hans Date: Thu, 7 Jan 2021 12:15:27 -0700 Subject: Add guide: Detect anomalies in nodes and applications with Netdata (#10451) * Init new anomalies guide * Continuing work * Finish draft of part 1 * Tweak to opening * Change headline * Copyediting and fixes * Fix link * Remove with Netdata from title * Clean up frontmatter * Update docs/guides/monitor/anomaly-detection.md Co-authored-by: Andrew Maguire * Update docs/guides/monitor/anomaly-detection.md Co-authored-by: Andrew Maguire * Fixes for Jen * Fixes and improvements for Andy Co-authored-by: Andrew Maguire --- collectors/python.d.plugin/anomalies/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) (limited to 'collectors') diff --git a/collectors/python.d.plugin/anomalies/README.md b/collectors/python.d.plugin/anomalies/README.md index 487a11a095..e074be6f52 100644 --- a/collectors/python.d.plugin/anomalies/README.md +++ b/collectors/python.d.plugin/anomalies/README.md @@ -1,15 +1,17 @@ -# Anomalies: ML-driven anomaly detection for the Netdata Agent +# Anomaly detection with Netdata This collector uses the Python [PyOD](https://pyod.readthedocs.io/en/latest/index.html) library to perform unsupervised [anomaly detection](https://en.wikipedia.org/wiki/Anomaly_detection) on your Netdata charts and/or dimensions. Instead of this collector just _collecting_ data, it also does some computation on the data it collects to return an anomaly probability and anomaly flag for each chart or custom model you define. This computation consists of a **train** function that runs every `train_n_secs` to train the ML models to learn what 'normal' typically looks like on your node. At each iteration there is also a **predict** function that uses the latest trained models and most recent metrics to produce an anomaly probability and anomaly flag for each chart or custom model you define. -**Note**: As this is a somewhat unique collector and involves often subjective concepts like anomalies and anomaly probabilities, we would love to hear any feedback on it from the community. Please let us know on the [community forum](https://community.netdata.cloud/c/agent-development/9) or drop us a note at [analytics-ml-team@netdata.cloud](mailto:analytics-ml-team@netdata.cloud) for any and all feedback, both positive and negative. This sort of feedback is priceless to help us make complex features more useful. +> As this is a somewhat unique collector and involves often subjective concepts like anomalies and anomaly probabilities, we would love to hear any feedback on it from the community. Please let us know on the [community forum](https://community.netdata.cloud/c/agent-development/9) or drop us a note at [analytics-ml-team@netdata.cloud](mailto:analytics-ml-team@netdata.cloud) for any and all feedback, both positive and negative. This sort of feedback is priceless to help us make complex features more useful. ## Charts -- cgit v1.2.3