[8.9] [DOCS] Improves documentation about deploying DFA trained models (backport #2524)

docs/en/stack/ml/df-analytics/ml-dfa-classification.asciidoc

@@ -258,6 +258,8 @@ The model that you created is stored as {es} documents in internal indices. In
 other words, the characteristics of your trained model are saved and ready to be 
 deployed and used as functions.
 
+include::ml-dfa-shared.asciidoc[tag=dfa-deploy-model]
+
 
 [discrete]
 [[ml-inference-class]]

docs/en/stack/ml/df-analytics/ml-dfa-regression.asciidoc

@@ -194,6 +194,8 @@ deployed and used as functions. The <<ml-inference-reg,{infer}>> feature enables
 you to use your model in a preprocessor of an ingest pipeline or in a pipeline 
 aggregation of a search query to make predictions about your data.
 
+include::ml-dfa-shared.asciidoc[tag=dfa-deploy-model]
+
 
 [discrete]
 [[ml-inference-reg]]

docs/en/stack/ml/df-analytics/ml-dfa-shared.asciidoc

@@ -1,3 +1,40 @@
+tag::dfa-deploy-model[]
+. To deploy {dfanalytics} model in a pipeline, navigate to  **Machine Learning** > 
+**Model Management** > **Trained models** in {kib}.
+
+. Find the model you want to deploy in the list and click **Deploy model** in 
+the **Actions** menu.
++
+--
+[role="screenshot"]
+image::images/ml-dfa-trained-models-ui.png["The trained models UI in {kib}"]
+--
+
+. Create an {infer} pipeline to be able to use the model against new data 
+through the pipeline. Add a name and a description or use the default values.
++
+--
+[role="screenshot"]
+image::images/ml-dfa-inference-pipeline.png["Creating an inference pipeline"]
+--
+
+. Configure the pipeline processors or use the default settings.
++
+--
+[role="screenshot"]
+image::images/ml-dfa-inference-processor.png["Configuring an inference processor"]
+--
+. Configure to handle ingest failures or use the default settings. 
+
+. (Optional) Test your pipeline by running a simulation of the pipeline to 
+  confirm it produces the anticipated results.
+
+. Review the settings and click **Create pipeline**.
+
+The model is deployed and ready to use through the {infer} pipeline.
+end::dfa-deploy-model[]
+
+
 tag::dfa-evaluation-intro[]
 Using the {dfanalytics} features to gain insights from a data set is an 
 iterative process. After you defined the problem you want to solve, and chose 
@@ -18,10 +55,8 @@ the ground truth. The {evaluatedf-api} evaluates the performance of the
 end::dfa-evaluation-intro[]
 
 tag::dfa-inference[]
-{infer-cap} is a {ml} feature that enables you to use supervised {ml} processes 
-– like {regression} or {classification} – not only as a batch analysis but in a 
-continuous fashion. This means that {infer} makes it possible to use 
-<<ml-trained-models,trained {ml} models>> against incoming data.
+{infer-cap} enables you to use <<ml-trained-models,trained {ml} models>> against 
+incoming data in a continuous fashion.
 
 For instance, suppose you have an online service and you would like to predict 
 whether a customer is likely to churn. You have an index with historical data – 
@@ -43,7 +78,7 @@ are indexed into the destination index.
 
 Check the {ref}/inference-processor.html[{infer} processor] and 
 {ref}/ml-df-analytics-apis.html[the {ml} {dfanalytics} API documentation] to 
-learn more about the feature.
+learn more.
 end::dfa-inference-processor[]
 
 tag::dfa-inference-aggregation[]
@@ -58,7 +93,7 @@ to set up a processor in the ingest pipeline.
 Check the 
 {ref}/search-aggregations-pipeline-inference-bucket-aggregation.html[{infer} bucket aggregation] 
 and {ref}/ml-df-analytics-apis.html[the {ml} {dfanalytics} API documentation] to 
-learn more about the feature.
+learn more.
 
 NOTE: If you use trained model aliases to reference your trained model in an 
 {infer} processor or {infer} aggregation, you can replace your trained model 

docs/en/stack/ml/df-analytics/ml-trained-models.asciidoc

@@ -12,14 +12,6 @@ information about this process, see <<ml-supervised-workflow>> and
 <<ml-inference-class,{infer} for {classification}>> and 
 <<ml-inference-reg,{regression}>>.
 
-You can also supply trained models that are not created by {dfanalytics-job} but
-adhere to the appropriate
-https://github.com/elastic/ml-json-schemas[JSON schema]. Likewise, you can use
-third-party models to perform natural language processing (NLP) tasks. If you
-want to use these trained models in the {stack}, you must store them in {es}
-documents by using the {ref}/put-trained-models.html[create trained models API].
-For more information about NLP models, refer to <<ml-nlp-deploy-models>>.
-
 In {kib}, you can view and manage your trained models in
 *{stack-manage-app}* > *Alerts and Insights* > *{ml-app}* and
 *{ml-app}* > *Model Management*.
@@ -28,6 +20,27 @@ Alternatively, you can use APIs like
 {ref}/get-trained-models.html[get trained models] and
 {ref}/delete-trained-models.html[delete trained models].
 
+[discrete]
+[[deploy-dfa-trained-models]]
+== Deploying trained models
+
+[discrete]
+=== Models trained by {dfanalytics}
+
+include::ml-dfa-shared.asciidoc[tag=dfa-deploy-model]
+
+
+[discrete]
+=== Models trained by other methods
+
+You can also supply trained models that are not created by {dfanalytics-job} but
+adhere to the appropriate
+https://github.com/elastic/ml-json-schemas[JSON schema]. Likewise, you can use
+third-party models to perform natural language processing (NLP) tasks. If you
+want to use these trained models in the {stack}, you must store them in {es}
+documents by using the {ref}/put-trained-models.html[create trained models API].
+For more information about NLP models, refer to <<ml-nlp-deploy-models>>.
+
 
 [discrete]
 [[export-import]]