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[DOCS] Adds AIOps section and explain log rate spikes docs (#138485)

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Co-authored-by: Lisa Cawley <lcawley@elastic.co>
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István Zoltán Szabó 2022-08-11 12:25:57 +02:00 • committed by GitHub
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@ -98,3 +98,48 @@ image::user/ml/images/outliers.png[{oldetection-cap} results in {kib}]
For more information about the {dfanalytics} feature, see
{ml-docs}/ml-dfanalytics.html[{ml-cap} {dfanalytics}].
[[xpack-ml-aiops]]
== AIOps
AIOps is a part of {ml-app} in {kib} which provides features that use advanced
statistical methods to help you interpret your data and its behavior.
[discrete]
[[explain-log-rate-spikes]]
=== Explain log rate spikes
preview::[]
Explain log rate spikes is a feature that uses advanced statistical methods to
identify reasons for increases in log rates. It makes it easy to find and
investigate causes of unusual spikes by using the analysis workflow view.
Examine the histogram chart of the log rates for a given {data-source}, and find
the reason behind a particular change possibly in millions of log events across
multiple fields and values.
You can find explain log rate spikes under **{ml-app}** > **AIOps** where you
can select the {data-source} or saved search that you want to analyze.
[role="screenshot"]
image::user/ml/images/ml-explain-log-rate-before.png[Log event histogram chart]
Select a spike in the log event histogram chart to start the analysis. It
identifies statistically significant field-value combinations that contribute to
the spike and displays them in a table. The table also shows an indicator of the
level of impact and a sparkline showing the shape of the impact in the chart.
Hovering over a row displays the impact on the histogram chart in more detail.
You can also pin a table row by clicking on it then move the cursor to the
histogram chart. It displays a tooltip with exact count values for the pinned
field which enables closer investigation.
Brushes in the chart show the baseline time range and the deviation in the
analyzed data. You can move the brushes to redefine both the baseline and the
deviation and rerun the analysis with the modified values.
[role="screenshot"]
image::user/ml/images/ml-explain-log-rate.png[Log rate spike explained]