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authorAndrew Maguire <andrewm4894@gmail.com>2022-06-01 15:11:23 +0100
committerGitHub <noreply@github.com>2022-06-01 17:11:23 +0300
commit044d4c9d916fbc94a5a00324ab377d56b4937a29 (patch)
tree10d5b30680699d522262e4b9c5a437c736a210a9
parenta16fbcef1649b898617239dd108c8515618c3488 (diff)
add note about anomaly advisor (#13042)
-rw-r--r--collectors/python.d.plugin/anomalies/README.md2
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@@ -7,6 +7,8 @@ sidebar_url: Anomalies
# Anomaly detection with Netdata
+**Note**: Check out the [Netdata Anomaly Advisor](https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor) for a more native anomaly detection experience within 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.