diff options
| author | Joel Hans <joel@netdata.cloud> | 2021-01-07 12:15:27 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-01-07 12:15:27 -0700 |
| commit | c4941b49274d82c2d9ee535a7fdaf932da2ebbf7 (patch) | |
| tree | 9ab50f416a599e0bb0474277f8c4338d5a4c69f1 /collectors | |
| parent | 46a8075c8ff6da974d1b39f5b3b9eb50385892c1 (diff) | |
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 <andrewm4894@gmail.com>
* Update docs/guides/monitor/anomaly-detection.md
Co-authored-by: Andrew Maguire <andrewm4894@gmail.com>
* Fixes for Jen
* Fixes and improvements for Andy
Co-authored-by: Andrew Maguire <andrewm4894@gmail.com>
Diffstat (limited to 'collectors')
| -rw-r--r-- | collectors/python.d.plugin/anomalies/README.md | 8 |
1 files changed, 5 insertions, 3 deletions
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 @@ <!-- -title: "Anomalies" +title: "Anomaly detection with Netdata" +description: "Use ML-driven anomaly detection to narrow your focus to only affected metrics and services/processes on your node to shorten root cause analysis." custom_edit_url: https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/anomalies/README.md +sidebar_url: anomalies --> -# 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 |