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@ -4,14 +4,9 @@
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As datasets increase in size and complexity, the human effort required to
|
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inspect dashboards or maintain rules for spotting infrastructure problems,
|
||||
cyber attacks, or business issues becomes impractical. The Elastic {ml}
|
||||
{anomaly-detect} feature automatically models the normal behavior of your time
|
||||
series data — learning trends, periodicity, and more — in real time to identify
|
||||
anomalies, streamline root cause analysis, and reduce false positives.
|
||||
|
||||
{anomaly-detect-cap} runs in and scales with {es}, and includes an
|
||||
intuitive UI on the {kib} *Machine Learning* page for creating {anomaly-jobs}
|
||||
and understanding results.
|
||||
cyber attacks, or business issues becomes impractical. Elastic {ml-features}
|
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such as {anomaly-detect} and {oldetection} make it easier to notice suspicious
|
||||
activities with minimal human interference.
|
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|
||||
If you have a basic license, you can use the *Data Visualizer* to learn more
|
||||
about your data. In particular, if your data is stored in {es} and contains a
|
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|
@ -25,9 +20,20 @@ experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in
|
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size). The *Data Visualizer* identifies the file format and field mappings. You
|
||||
can then optionally import that data into an {es} index.
|
||||
|
||||
If you have a trial or platinum license, you can
|
||||
create {anomaly-jobs} and manage jobs and {dfeeds} from the *Job
|
||||
Management* pane:
|
||||
[float]
|
||||
[[xpack-ml-anomalies]]
|
||||
=== {anomaly-detect-cap}
|
||||
|
||||
The Elastic {ml} {anomaly-detect} feature automatically models the normal
|
||||
behavior of your time series data — learning trends, periodicity, and more — in
|
||||
real time to identify anomalies, streamline root cause analysis, and reduce
|
||||
false positives. {anomaly-detect-cap} runs in and scales with {es}, and
|
||||
includes an intuitive UI on the {kib} *Machine Learning* page for creating
|
||||
{anomaly-jobs} and understanding results.
|
||||
|
||||
If you have a license that includes the {ml-features}, you can
|
||||
create {anomaly-jobs} and manage jobs and {dfeeds} from the *Job Management*
|
||||
pane:
|
||||
|
||||
[role="screenshot"]
|
||||
image::user/ml/images/ml-job-management.jpg[Job Management]
|
||||
|
@ -64,6 +70,23 @@ browser so that it does not block pop-up windows or create an exception for your
|
|||
{kib} URL.
|
||||
|
||||
For more information about the {anomaly-detect} feature, see
|
||||
https://www.elastic.co/what-is/elastic-stack-machine-learning and
|
||||
{stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}].
|
||||
https://www.elastic.co/what-is/elastic-stack-machine-learning[{ml-cap} in the {stack}]
|
||||
and {stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}].
|
||||
|
||||
[float]
|
||||
[[xpack-ml-dfanalytics]]
|
||||
=== {dfanalytics-cap}
|
||||
|
||||
The Elastic {ml} {dfanalytics} feature enables you to analyze your data using
|
||||
{oldetection} and {regression} algorithms and generate new indices that contain
|
||||
the results alongside your source data.
|
||||
|
||||
If you have a license that includes the {ml-features}, you can create
|
||||
{oldetection} {dfanalytics-jobs} and view their results on the *Analytics* page
|
||||
in {kib}. For example:
|
||||
|
||||
[role="screenshot"]
|
||||
image::user/ml/images/outliers.jpg[{oldetection-cap} results in {kib}]
|
||||
|
||||
For more information about the {dfanalytics} feature, see
|
||||
{stack-ov}/ml-dfanalytics.html[{ml-cap} {dfanalytics}].
|
||||
|
|