[7.3] [DOCS] Removes Extend your use case section (#49812) (#49920)

docs/redirects.asciidoc

@@ -49,4 +49,9 @@ This page has moved. Please see {stack-ov}/create-jobs.html[Creating {anomaly-jo
 [role="exclude",id="job-tips"]
 == Machine learning job tips
 
-This page has moved. Please see {stack-ov}/create-jobs.html#job-tips[Machine learning job tips].
+This page has moved. Please see {stack-ov}/create-jobs.html#job-tips[Machine learning job tips].
+
+[role="exclude",id="extend"]
+== Extend your use case
+
+This page was deleted. See <<xpack-graph>> and <<xpack-ml>>.

docs/user/graph/configuring-graph.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[graph-configuration]]
-=== Configuring Graph
+== Configuring Graph
 
 When a user saves a graph workspace in Kibana, it is stored in the `.kibana`
 index along with other saved objects like visualizations and dashboards.
@@ -57,9 +57,9 @@ is displayed. For more information on granting access to Kibana, see
 [role="screenshot"]
 image::user/graph/images/graph-read-only-badge.png[Example of Graph's read only access indicator in Kibana's header]
 
-[float]
+[discrete]
 [[disable-drill-down]]
-==== Disabling drill down configuration
+=== Disabling drill down configuration
 
 By default, users can configure _drill down_ URLs to display additional
 information about a selected vertex in a new browser window. For example,

docs/user/graph/getting-started.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[graph-getting-started]]
-=== Using Graph
+== Using Graph
 
 Graph is automatically enabled in {es} and {kib}.
 

docs/user/graph/index.asciidoc

@@ -1,7 +1,9 @@
 [role="xpack"]
 [[xpack-graph]]
-== Graph data connections
+= Graph data connections
 
+[partintro]
+--
 The {graph-features} enable you to discover how items in an
 Elasticsearch index are related. You can explore the connections between
 indexed terms and see which connections are the most meaningful. This can be
@@ -17,9 +19,9 @@ and an interactive graph visualization tool for Kibana. Both work out of the
 box with existing Elasticsearch indices--you don't need to store any
 additional data to use these features.
 
+[discrete]
 [[how-graph-works]]
-[float]
-=== How Graph works
+== How Graph works
 The graph API provides an alternative way to extract and summarize information
 about the documents and terms in your Elasticsearch index. A _graph_ is really
 just a network of related items. In our case, this means a network of related
@@ -62,6 +64,7 @@ multi-node clusters and scales with your Elasticsearch deployment.
 Advanced options let you control how your data is sampled and summarized.
 You can also set timeouts to prevent graph queries from adversely
 affecting the cluster.
+--
 
 include::getting-started.asciidoc[]
 

docs/user/graph/limitations.asciidoc

@@ -1,12 +1,12 @@
 [role="xpack"]
 [[graph-limitations]]
-=== Graph limitations
+== Graph limitations
 ++++
 <titleabbrev>Limitations</titleabbrev>
 ++++
 
-[float]
-==== Limited support for multiple indices
+[discrete]
+=== Limited support for multiple indices
 The graph API can explore multiple indices, types, or aliases in a
 single API request, but the assumption is that each "hop" it performs
 is querying the same set of indices. Currently, it is not possible to

docs/user/graph/troubleshooting.asciidoc

@@ -1,12 +1,12 @@
 [role="xpack"]
 [[graph-troubleshooting]]
-=== Graph Troubleshooting
+== Graph Troubleshooting
 ++++
 <titleabbrev>Troubleshooting</titleabbrev>
 ++++
 
-[float]
-==== Why are results missing?
+[discrete]
+=== Why are results missing?
 
 The default settings in Graph API requests are configured to tune out noisy
 results by using the following strategies:
@@ -29,8 +29,8 @@ of any statistical correlation with the sample.
 * Set the `min_doc_count` for your vertices to 1 to ensure only one document is
 required to assert a relationship.
 
-[float]
-==== What can I do to to improve performance?
+[discrete]
+=== What can I do to to improve performance?
 
 With the default setting of `use_significance` set to `true`, the Graph API
 performs a background frequency check of the terms it discovers as part of

docs/user/index.asciidoc

@@ -16,7 +16,9 @@ include::dashboard.asciidoc[]
 
 include::canvas.asciidoc[]
 
-include::extend.asciidoc[]
+include::graph/index.asciidoc[]
+
+include::ml/index.asciidoc[]
 
 include::{kib-repo-dir}/maps/index.asciidoc[]
 

docs/user/ml/index.asciidoc

@@ -1,17 +1,14 @@
 [role="xpack"]
 [[xpack-ml]]
-== {ml-cap}
+= {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 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}
+such as {anomaly-detect} and {oldetection} make it easier to notice suspicious
+activities with minimal human interference.
 
 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
@@ -25,9 +22,22 @@ 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 
-create {anomaly-jobs} and manage jobs and {dfeeds} from the *Job 
-Management* pane: 
+
+--
+
+[[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]
@@ -67,3 +77,16 @@ 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}].
 
+[[xpack-ml-dfanalytics]]
+== {dfanalytics-cap}
+
+The Elastic {ml} {dfanalytics} feature enables you to analyze your data using
+{oldetection} 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 more information about the {dfanalytics} feature, see 
+{stack-ov}/ml-dfanalytics.html[{ml-cap} {dfanalytics}].