2017-05-19 16:40:50 -04:00
|
|
|
[[ml-configuring]]
|
|
|
|
== Configuring Machine Learning
|
|
|
|
|
|
|
|
If you want to use {xpackml} features, there must be at least one {ml} node in
|
|
|
|
your cluster and all master-eligible nodes must have {ml} enabled. By default,
|
|
|
|
when you install {xpack}, all nodes are {ml} nodes. For more information about
|
|
|
|
these settings, see <<ml-settings>>.
|
|
|
|
|
|
|
|
To use the {xpackml} features to analyze your data, you must create a job and
|
|
|
|
send your data to that job.
|
|
|
|
|
|
|
|
* If your data is stored in {es}:
|
|
|
|
|
|
|
|
** You can create a {dfeed}, which retrieves data from {es} for analysis.
|
|
|
|
** You can use {kib} to expedite the creation of jobs and {dfeeds}.
|
|
|
|
|
|
|
|
* If your data is not stored in {es}, you can <<ml-post-data,POST data>> from any
|
|
|
|
source directly to an API.
|
|
|
|
|
|
|
|
The results of {ml} analysis are stored in {es} and you can use {kib} to help
|
|
|
|
you visualize and explore the results.
|
|
|
|
|
|
|
|
For a tutorial that walks you through these configuration steps,
|
|
|
|
see <<ml-getting-started>>.
|
|
|
|
|
2017-05-23 17:34:21 -04:00
|
|
|
Though it is quite simple to analyze your data and provide quick {ml} results,
|
|
|
|
gaining deep insights might require some additional planning and configuration.
|
|
|
|
The scenarios in this section describe some best practices for generating useful
|
|
|
|
{ml} results and insights from your data.
|
2017-05-19 16:40:50 -04:00
|
|
|
|
2017-05-23 17:34:21 -04:00
|
|
|
* <<ml-configuring-aggregation>>
|
2017-06-12 13:41:14 -04:00
|
|
|
* <<ml-configuring-categories>>
|
2017-05-19 16:40:50 -04:00
|
|
|
|
2017-05-23 17:34:21 -04:00
|
|
|
include::aggregations.asciidoc[]
|
2017-06-12 13:41:14 -04:00
|
|
|
include::categories.asciidoc[]
|