[DOCS] Moves ml content into user folder (#45482) (#45550)

docs/user/index.asciidoc

@@ -20,7 +20,7 @@ include::timelion.asciidoc[]
 
 include::canvas.asciidoc[]
 
-include::{kib-repo-dir}/ml/index.asciidoc[]
+include::ml/index.asciidoc[]
 
 include::{kib-repo-dir}/maps/index.asciidoc[]
 

docs/user/ml/creating-jobs.asciidoc

@@ -8,7 +8,7 @@ necessary to perform an analytics task.
 {kib} provides the following wizards to make it easier to create jobs:
 
 [role="screenshot"]
-image::ml/images/ml-create-job.jpg[Create New Job]
+image::user/ml/images/ml-create-job.jpg[Create New Job]
 
 A _single metric job_ is a simple job that contains a single _detector_. A
 detector defines the type of analysis that will occur and which fields to
@@ -32,13 +32,13 @@ http://nginx.org/[Nginx] and https://httpd.apache.org/[Apache] HTTP servers to
 {es}, the following wizards appear:
 
 [role="screenshot"]
-image::ml/images/ml-data-recognizer-filebeat.jpg[A screenshot of the {filebeat} job creation wizards]
+image::user/ml/images/ml-data-recognizer-filebeat.jpg[A screenshot of the {filebeat} job creation wizards]
 
 Likewise, if you use {auditbeat-ref}/index.html[{auditbeat}] to audit process
 activity on your systems, the following wizards appear:
 
 [role="screenshot"]
-image::ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards]
+image::user/ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards]
 
 These wizards create {anomaly-jobs}, dashboards, searches, and visualizations that
 are customized to help you analyze your {auditbeat} and {filebeat} data.
@@ -64,4 +64,4 @@ advanced jobs:
 
 * https://www.elastic.co/videos/machine-learning-tutorial-creating-a-single-metric-job[Machine Learning for the Elastic Stack: Creating a single metric job]
 * https://www.elastic.co/videos/machine-learning-tutorial-creating-a-multi-metric-job[Machine Learning for the Elastic Stack: Creating a multi-metric job]
-* https://www.elastic.co/videos/machine-learning-lab-3-detect-outliers-in-a-population[Machine Learning for the Elastic Stack: Detect Outliers in a Population]
+* https://www.elastic.co/videos/machine-learning-lab-3-detect-outliers-in-a-population[Machine Learning for the Elastic Stack: Detect Outliers in a Population]

docs/user/ml/index.asciidoc

@@ -22,7 +22,7 @@ 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]
+image::user/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 
@@ -33,20 +33,20 @@ If you have a trial or platinum license, you can
 Management* pane: 
 
 [role="screenshot"]
-image::ml/images/ml-job-management.jpg[Job Management]
+image::user/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]
+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::ml/images/ml-single-metric-viewer.jpg[Single Metric Viewer]
+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
@@ -54,7 +54,7 @@ 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]
+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