* [ML] Add kibana setting for file data visualizers max file size * adding failsafe for setting * fixing id * [DOCS] Updates Data Visualizer setting Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com> Co-authored-by: lcawl <lcawley@elastic.co>
102 lines
No EOL
4.1 KiB
Text
102 lines
No EOL
4.1 KiB
Text
[role="xpack"]
|
|
[[xpack-ml]]
|
|
= {ml-cap}
|
|
|
|
[partintro]
|
|
--
|
|
As data sets increase in size and complexity, the human effort required to
|
|
inspect dashboards or maintain rules for spotting infrastructure problems,
|
|
cyber attacks, or business issues becomes impractical. Elastic {ml-features}
|
|
such as {anomaly-detect} and {oldetection} make it easier to notice suspicious
|
|
activities with minimal human interference.
|
|
|
|
{kib} includes a free *{data-viz}* to learn more about your data. In particular,
|
|
if your data is stored in {es} and contains a time field, you can use the
|
|
*{data-viz}* to identify possible fields for {anomaly-detect}:
|
|
|
|
[role="screenshot"]
|
|
image::user/ml/images/ml-data-visualizer-sample.jpg[{data-viz} for sample flight data]
|
|
|
|
experimental[] You can also upload a CSV, NDJSON, or log file. The *{data-viz}*
|
|
identifies the file format and field mappings. You can then optionally import
|
|
that data into an {es} index. To change the default file size limit, see
|
|
<<kibana-ml-settings>>.
|
|
|
|
You need the following permissions to use the {data-viz} with file upload:
|
|
|
|
* cluster privileges: `monitor`, `manage_ingest_pipelines`
|
|
* index privileges: `read`, `manage`, `index`
|
|
|
|
For more information, see {ref}/security-privileges.html[Security privileges]
|
|
and {ml-docs}/setup.html[Set up {ml-features}].
|
|
|
|
--
|
|
|
|
[[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]
|
|
|
|
You can use the *Settings* pane to create and edit
|
|
{ml-docs}/ml-calendars.html[calendars] and the filters that are used in
|
|
{ml-docs}/ml-rules.html[custom rules]:
|
|
|
|
[role="screenshot"]
|
|
image::user/ml/images/ml-settings.jpg[Calendar Management]
|
|
|
|
The *Anomaly Explorer* and *Single Metric Viewer* display the results of your
|
|
{anomaly-jobs}. For example:
|
|
|
|
[role="screenshot"]
|
|
image::user/ml/images/ml-single-metric-viewer.jpg[Single Metric Viewer]
|
|
|
|
You can optionally add annotations by drag-selecting a period of time in
|
|
the *Single Metric Viewer* and adding a description. For example, you can add an
|
|
explanation for anomalies in that time period or provide notes about what is
|
|
occurring in your operational environment at that time:
|
|
|
|
[role="screenshot"]
|
|
image::user/ml/images/ml-annotations-list.jpg[Single Metric Viewer with annotations]
|
|
|
|
In some circumstances, annotations are also added automatically. For example, if
|
|
the {anomaly-job} detects that there is missing data, it annotates the affected
|
|
time period. For more information, see
|
|
{ml-docs}/ml-delayed-data-detection.html[Handling delayed data]. The
|
|
*Job Management* pane shows the full list of annotations for each job.
|
|
|
|
NOTE: The {kib} {ml-features} use pop-ups. You must configure your web
|
|
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[{ml-cap} in the {stack}]
|
|
and {ml-docs}/xpack-ml.html[{ml-cap} {anomaly-detect}].
|
|
|
|
[[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
|
|
{ml-docs}/ml-dfanalytics.html[{ml-cap} {dfanalytics}]. |