[role="xpack"]
[[xpack-ml]]
= {ml-cap}

[partintro]
--

As datasets 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. The Elastic {ml} 
{anomaly-detect} feature automatically model 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} run in and scale with {es}, and include an
intuitive UI on the {kib} *Machine Learning* page for creating {anomaly-jobs} 
and understanding results.

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
time field, you can use the *Data Visualizer* to identify possible fields for
{anomaly-detect}:

[role="screenshot"]
image::ml/images/ml-data-visualizer-sample.jpg[Data Visualizer for sample flight data]

experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in 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 
<<ml-jobs,create {anomaly-jobs}>> and manage jobs and {dfeeds} from the *Job 
Management* pane: 

[role="screenshot"]
image::ml/images/ml-job-management.jpg[Job Management]

You can use the *Settings* pane to create and edit 
{stack-ov}/ml-calendars.html[calendars] and the filters that are used in 
{stack-ov}/ml-rules.html[custom rules]:

[role="screenshot"]
image::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::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::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 
{stack-ov}/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
{stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}].

--

include::creating-jobs.asciidoc[]
include::job-tips.asciidoc[]

