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diff --git a/docs/guides/monitor/anomaly-detection.md b/docs/guides/monitor/anomaly-detection.md index e98c5c02e..ce819d937 100644 --- a/docs/guides/monitor/anomaly-detection.md +++ b/docs/guides/monitor/anomaly-detection.md @@ -14,27 +14,27 @@ custom_edit_url: https://github.com/netdata/netdata/edit/master/docs/guides/moni As of [`v1.32.0`](https://github.com/netdata/netdata/releases/tag/v1.32.0), Netdata comes with some ML powered [anomaly detection](https://en.wikipedia.org/wiki/Anomaly_detection) capabilities built into it and available to use out of the box, with zero configuration required (ML was enabled by default in `v1.35.0-29-nightly` in [this PR](https://github.com/netdata/netdata/pull/13158), previously it required a one line config change). -This means that in addition to collecting raw value metrics, the Netdata agent will also produce an [`anomaly-bit`](https://learn.netdata.cloud/docs/agent/ml#anomaly-bit---100--anomalous-0--normal) every second which will be `100` when recent raw metric values are considered anomalous by Netdata and `0` when they look normal. Once we aggregate beyond one second intervals this aggregated `anomaly-bit` becomes an ["anomaly rate"](https://learn.netdata.cloud/docs/agent/ml#anomaly-rate---averageanomaly-bit). +This means that in addition to collecting raw value metrics, the Netdata agent will also produce an [`anomaly-bit`](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-bit---100--anomalous-0--normal) every second which will be `100` when recent raw metric values are considered anomalous by Netdata and `0` when they look normal. Once we aggregate beyond one second intervals this aggregated `anomaly-bit` becomes an ["anomaly rate"](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-rate---averageanomaly-bit). -To be as concrete as possible, the below api call shows how to access the raw anomaly bit of the `system.cpu` chart from the [london.my-netdata.io](https://london.my-netdata.io) Netdata demo server. Passing `options=anomaly-bit` returns the anomay bit instead of the raw metric value. +To be as concrete as possible, the below api call shows how to access the raw anomaly bit of the `system.cpu` chart from the [london.my-netdata.io](https://london.my-netdata.io) Netdata demo server. Passing `options=anomaly-bit` returns the anomaly bit instead of the raw metric value. ``` https://london.my-netdata.io/api/v1/data?chart=system.cpu&options=anomaly-bit ``` -If we aggregate the above to just 1 point by adding `points=1` we get an "[Anomaly Rate](https://learn.netdata.cloud/docs/agent/ml#anomaly-rate---averageanomaly-bit)": +If we aggregate the above to just 1 point by adding `points=1` we get an "[Anomaly Rate](https://github.com/netdata/netdata/blob/master/ml/README.md#anomaly-rate---averageanomaly-bit)": ``` https://london.my-netdata.io/api/v1/data?chart=system.cpu&options=anomaly-bit&points=1 ``` -The fundamentals of Netdata's anomaly detection approach and implmentation are covered in lots more detail in the [agent ML documentation](https://learn.netdata.cloud/docs/agent/ml). +The fundamentals of Netdata's anomaly detection approach and implementation are covered in lots more detail in the [agent ML documentation](https://github.com/netdata/netdata/blob/master/ml/README.md). This guide will explain how to get started using these ML based anomaly detection capabilities within Netdata. ## Anomaly Advisor -The [Anomaly Advisor](https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor) is the flagship anomaly detection feature within Netdata. In the "Anomalies" tab of Netdata you will see an overall "Anomaly Rate" chart that aggregates node level anomaly rate for all nodes in a space. The aim of this chart is to make it easy to quickly spot periods of time where the overall "[node anomaly rate](https://learn.netdata.cloud/docs/agent/ml#node-anomaly-rate)" is evelated in some unusual way and for what node or nodes this relates to. +The [Anomaly Advisor](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.mdx) is the flagship anomaly detection feature within Netdata. In the "Anomalies" tab of Netdata you will see an overall "Anomaly Rate" chart that aggregates node level anomaly rate for all nodes in a space. The aim of this chart is to make it easy to quickly spot periods of time where the overall "[node anomaly rate](https://github.com/netdata/netdata/blob/master/ml/README.md#node-anomaly-rate)" is elevated in some unusual way and for what node or nodes this relates to.  @@ -44,7 +44,7 @@ Once an area on the Anomaly Rate chart is highlighted netdata will append a "hea ## Embedded Anomaly Rate Charts -Charts in both the [Overview](https://learn.netdata.cloud/docs/cloud/visualize/overview) and [single node dashboard](https://learn.netdata.cloud/docs/cloud/visualize/overview#jump-to-single-node-dashboards) tabs also expose the underlying anomaly rates for each dimension so users can easily see if the raw metrics are considered anomalous or not by Netdata. +Charts in both the [Overview](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/overview.md) and [single node dashboard](https://github.com/netdata/netdata/blob/master/docs/cloud/visualize/overview.md#jump-to-single-node-dashboards) tabs also expose the underlying anomaly rates for each dimension so users can easily see if the raw metrics are considered anomalous or not by Netdata. Pressing the anomalies icon (next to the information icon in the chart header) will expand the anomaly rate chart to make it easy to see how the anomaly rate for any individual dimension corresponds to the raw underlying data. In the example below we can see that the spike in `system.pgpgio|in` corresponded in the anomaly rate for that dimension jumping to 100% for a small period of time until the spike passed. @@ -65,9 +65,9 @@ You can see some example ML based alert configurations below: Check out the resources below to learn more about how Netdata is approaching ML: -- [Agent ML documentation](https://learn.netdata.cloud/docs/agent/ml). -- [Anomaly Advisor documentation](https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor). -- [Metric Correlations documentation](https://learn.netdata.cloud/docs/cloud/insights/metric-correlations). +- [Agent ML documentation](https://github.com/netdata/netdata/blob/master/ml/README.md). +- [Anomaly Advisor documentation](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.mdx). +- [Metric Correlations documentation](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/metric-correlations.md). - Anomaly Advisor [launch blog post](https://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata/). - Netdata Approach to ML [blog post](https://www.netdata.cloud/blog/our-approach-to-machine-learning/). - `areal/ml` related [GitHub Discussions](https://github.com/netdata/netdata/discussions?discussions_q=label%3Aarea%2Fml). |