Updates phrasing when referring to pages

docs/en/stack/ml/anomaly-detection/ml-ad-run-jobs.asciidoc

@@ -33,8 +33,8 @@ a {dfeed} will be required.
 You can create {anomaly-jobs} by using the
 {ref}/ml-put-job.html[create {anomaly-jobs} API]. {kib} also provides
 wizards to simplify the process, which vary depending on whether you are using
-the {ml-app} app, {security-app} or {observability} apps. In *{ml-app}* >
-*Anomaly Detection*:
+the {ml-app} app, {security-app} or {observability} apps. To open *Anomaly Detection*, 
+find *{ml-app}* in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 
 [role="screenshot"]
 image::images/ml-create-job.png[Create New Job]

docs/en/stack/ml/anomaly-detection/ml-detect-categories.asciidoc

@@ -33,7 +33,7 @@ Avoid using human-generated data for categorization analysis.
 [[creating-categorization-jobs]]
 == Creating categorization jobs
 
-. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**.
+. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 . Click **Create job**, select the {data-view} you want to analyze.
 . Select the **Categorization** wizard from the list.
 . Choose a categorization detector - it's the `count` function in this example - and the field you want to categorize - the `message` field in this example.

docs/en/stack/ml/anomaly-detection/ml-jobs-from-visuals.asciidoc

@@ -40,7 +40,7 @@ NOTE: You need to have a compatible visualization on **Dashboard** to create an
 which is based on the {kib} sample flight data set. Select the `Flight count` 
 visualization from the dashboard.
 
-. Go to **Analytics > Dashboard** and select a dashboard with a compatible 
+. Go to **Analytics > Dashboard** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. Select a dashboard with a compatible 
 visualization.
 . Open the **Options (...) menu** for the panel, then select **More**.  
 . Select **Create {anomaly-job}**. The option is only displayed if the 

docs/en/stack/ml/anomaly-detection/ml-population-analysis.asciidoc

@@ -27,7 +27,7 @@ Population analysis is resource-efficient and scales well, enabling the analysis
 [[creating-population-jobs]]
 == Creating population jobs
 
-. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**.
+. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 . Click **Create job**, select the {data-source} you want to analyze.
 . Select the **Population** wizard from the list.
 . Choose a population field - it's the `clientip` field in this example - and the metric you want to use for the analysis - `Mean(bytes)` in this example.

docs/en/stack/ml/anomaly-detection/ml-revert-model-snapshot.asciidoc

@@ -7,7 +7,7 @@ resilience. It makes it possible to reset the model to a previous state in case
 of a system failure or if the model changed significantly due to a one-off 
 event.
 
-. In {kib}, navigate to **{ml-app} > Anomaly Detection > Jobs**.
+. In {kib}, navigate to *Jobs*. To open *Jobs*, find **{ml-app} > Anomaly Detection** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 . Locate the {anomaly-job} whose model you want to revert in the job table.
 . Open the job details and navigate to the **Model Snapshots** tab.
 +

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

@@ -1,6 +1,6 @@
 tag::dfa-deploy-model[]
 . To deploy {dfanalytics} model in a pipeline, navigate to  **Machine Learning** > 
-**Model Management** > **Trained models** in {kib}.
+**Model Management** > **Trained models** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}.
 
 . Find the model you want to deploy in the list and click **Deploy model** in 
 the **Actions** menu.

docs/en/stack/ml/get-started/ml-gs-results.asciidoc

@@ -34,7 +34,7 @@ request rate on your web site drops significantly.
 
 Let's start by looking at this simple job in the **Single Metric Viewer**:
 
-. Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your
+. Select the *Jobs* tab in *{ml-app}* to see the list of your
 {anomaly-jobs}.
 
 . Click the chart icon in the *Actions* column for your `low_request_rate` job
@@ -151,7 +151,7 @@ look at both high and low request rates partitioned by response code.
 Let's start by looking at the `response_code_rates` job in the
 **Anomaly Explorer**:
 
-. Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your
+. Select the *Jobs* tab in *{ml-app}* to see the list of your
 {anomaly-jobs}.
 
 . Open the `response_code_rates` job in the Anomaly Explorer to view its results

docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc

@@ -17,7 +17,7 @@ exception for your {kib} URL.
 
 --
 
-. Click *Machine Learning* in the {kib} main menu.
+. Open *Machine Learning* from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 
 . Select the *{data-viz}* tab.
 

docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc

@@ -92,7 +92,7 @@ NOTE: For most cases, the preferred version is the **Intel and Linux optimized**
 [[trained-model-e5]]
 ==== Using the Trained Models page
 
-1. In {kib}, navigate to **{ml-app}** > **Trained Models**. E5 can be found in 
+1. In {kib}, navigate to **{ml-app}** > **Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]. E5 can be found in 
 the list of trained models. There are two versions available: one portable 
 version which runs on any hardware and one version which is optimized for Intel® 
 silicon. You can see which model is recommended to use based on your hardware 
@@ -250,7 +250,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/`
 . Repeat step 2 and step 3 on all master-eligible nodes.
 . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the 
 master-eligible nodes one by one.
-. Navigate to the **Trained Models** page in {kib}, E5 can be found in the 
+. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. E5 can be found in the 
 list of trained models.
 . Click the **Add trained model** button, select the E5 model version you 
 downloaded in step 1 and want to deploy and click **Download**. The selected 

docs/en/stack/ml/nlp/ml-nlp-elser.asciidoc

@@ -350,7 +350,7 @@ master-eligible nodes can reach the server you specify.
 . Repeat step 5 on all master-eligible nodes.
 . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the 
 master-eligible nodes one by one.
-. Navigate to the **Trained Models** page in {kib}, ELSER can be found in the 
+. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the 
 list of trained models.
 . Click the **Add trained model** button, select the ELSER model version you 
 downloaded in step 1 and want to deploy, and click **Download**. The selected 
@@ -390,7 +390,7 @@ xpack.ml.model_repository: file://${path.home}/config/models/`
 . Repeat step 2 and step 3 on all master-eligible nodes.
 . {ref}/restart-cluster.html#restart-cluster-rolling[Restart] the 
 master-eligible nodes one by one.
-. Navigate to the **Trained Models** page in {kib}, ELSER can be found in the 
+. Navigate to the **Trained Models** page from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. ELSER can be found in the 
 list of trained models.
 . Click the **Add trained model** button, select the ELSER model version you 
 downloaded in step 1 and want to deploy and click **Download**. The selected 
@@ -406,7 +406,7 @@ allocations and threads per allocation values.
 == Testing ELSER
 
 You can test the deployed model in {kib}. Navigate to **Model Management** > 
-**Trained Models**, locate the deployed ELSER model in the list of trained 
+**Trained Models** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar] in {kib}. Locate the deployed ELSER model in the list of trained 
 models, then select **Test model** from the Actions menu.
 
 You can use data from an existing index to test the model. Select the index, 

docs/en/stack/ml/nlp/ml-nlp-inference.asciidoc

@@ -18,7 +18,7 @@ can use it to perform {nlp} tasks in ingest pipelines.
 == Add an {infer} processor to an ingest pipeline
 
 In {kib}, you can create and edit pipelines in **{stack-manage-app}** >
-**Ingest Pipelines**.
+**Ingest Pipelines**. To open **Ingest Pipelines**, find **{stack-manage-app}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 
 [role="screenshot"]
 image::images/ml-nlp-pipeline-lang.png[Creating a pipeline in the Stack Management app,align="center"]

docs/en/stack/ml/nlp/ml-nlp-ner-example.asciidoc

@@ -294,7 +294,7 @@ You can create a tag cloud to visualize your data processed by the {infer}
 pipeline. A tag cloud is a visualization that scales words by the frequency at 
 which they occur. It is a handy tool for viewing the entities found in the data.
 
-In {kib}, open **Stack management** > **{data-sources-cap}**, and create a new 
+In {kib}, open **Stack management** > **{data-sources-cap}** from the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar], and create a new 
 {data-source} from the `les-miserables-infer` index pattern. 
 
 Open **Dashboard** and create a new dashboard. Select the 

docs/en/stack/ml/setup.asciidoc

@@ -11,17 +11,16 @@
 
 To use the {stack} {ml-features}, you must have:
 
-[%interactive]
-- [ ] the {subscriptions}[appropriate subscription] level or the free trial 
+- the {subscriptions}[appropriate subscription] level or the free trial 
   period activated
-- [ ] `xpack.ml.enabled` set to its default value of `true` on every node in the 
+- `xpack.ml.enabled` set to its default value of `true` on every node in the 
   cluster (refer to {ref}/ml-settings.html[{ml-cap} settings in {es}])
-- [ ] `ml` value defined in the list of `node.roles` on the 
+- `ml` value defined in the list of `node.roles` on the 
   {ref}/modules-node.html#ml-node[{ml} nodes]
-- [ ] {ml} features visible in the {kib} space
-- [ ] security privileges assigned to the user that:
-    * grant use of {ml-features}, and
-    * grant access to source and destination indices.
+- {ml} features visible in the {kib} space
+- security privileges assigned to the user that:
+  * grant use of {ml-features}, and
+  * grant access to source and destination indices.
 
 TIP: The fastest way to get started with {ml-features} is to
 {ess-trial}[start a free 14-day trial of {ess}] in the cloud.
@@ -39,12 +38,15 @@ the two main categories:
 * *<<kib-security-privileges>>*: uses the {ml-features} in {kib} and does not 
 use Dev Tools. It requires either {kib} feature privileges or {es} security 
 privileges and is granted the most permissive combination of both. {kib} feature 
-privileges are recommended if you control job level visibility via _Spaces_. 
+privileges are recommended if you control job level visibility via **Spaces**. 
 {ml-cap} features must be visible in the relevant space. Refer to 
 <<kib-visibility-spaces>> for configuration information.
 
-You can configure these privileges under **{stack-manage-app}** > _Security_ in 
-{kib} or via the respective {es} security APIs.
+You can configure these privileges
+
+- under **Security**. To open Security, find **{stack-manage-app}** in the main menu or
+use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
+- via the respective {es} security APIs.
 
 
 [discrete]
@@ -55,19 +57,17 @@ If you use {ml} APIs, you must have the following cluster and index privileges:
 
 For full access:
 
-[%interactive]
-* [ ] `machine_learning_admin` built-in role or the equivalent cluster 
+* `machine_learning_admin` built-in role or the equivalent cluster 
 privileges 
-* [ ] `read` and `view_index_metadata` on source indices
-* [ ] `read`, `manage`, and `index` on destination indices (for 
+* `read` and `view_index_metadata` on source indices
+* `read`, `manage`, and `index` on destination indices (for 
   {dfanalytics-jobs} only)
 
 For read-only access:
 
-[%interactive]
-* [ ] `machine_learning_user` built-in role or the equivalent cluster privileges
-* [ ] `read` index privileges on source indices
-* [ ] `read` index privileges on destination indices (for {dfanalytics-jobs}
+* `machine_learning_user` built-in role or the equivalent cluster privileges
+* `read` index privileges on source indices
+* `read` index privileges on destination indices (for {dfanalytics-jobs}
   only)
 
 IMPORTANT: The `machine_learning_admin` and `machine_learning_user` built-in
@@ -92,19 +92,21 @@ visualizations as well as {ml} job, trained model and module saved objects.
 
 In {kib}, the {ml-features} must be visible in your
 {kibana-ref}/xpack-spaces.html#spaces-control-feature-visibility[space]. To 
-control which features are visible in your space, use **{stack-manage-app}** > 
-_{kib}_ > _Spaces_.
+manage which features are visible in your space, go to **{stack-manage-app}** > 
+**{kib}** > **Spaces** or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar]
+to locate **Spaces** directly.
 
 [role="screenshot"]
 image::spaces.jpg["Manage spaces in {kib}"]
 
 In addition to index privileges, source {data-sources} must also exist in the 
-same space as your {ml} jobs. These can be configured in **{stack-manage-app}** 
-> _{kib}_ > _{data-sources-caps}_.
+same space as your {ml} jobs. You can configure these under **{data-sources-caps}**. To open **{data-sources-caps}**,
+find **{stack-manage-app}** > **{kib}** in the main menu, or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 
 
 Each {ml} job and trained model can be assigned to all, one, or multiple spaces.
-This can be configured in **{stack-manage-app} > Alerts and Insights > Machine Learning**.
+This can be configured in **Machine Learning**. To open **Machine Learning**, find **{stack-manage-app} > Alerts and Insights** in the main menu,
+or use the {kibana-ref}/kibana-concepts-analysts.html#_finding_your_apps_and_objects[global search bar].
 You can edit the spaces that a job or model is assigned to by clicking the
 icons in the **Spaces** column.
 
@@ -118,22 +120,20 @@ image::assign-job-spaces.jpg["Assign machine learning jobs to spaces"]
 
 Within a {kib} space, for full access to the {ml-features}, you must have:
 
-[%interactive]
-* [ ] `Machine Learning: All` {kib} privileges
-* [ ] `Data Views Management: All` {kib} feature privileges
-* [ ] `read`, and `view_index_metadata` index privileges on your source indices
-* [ ] {data-sources} for your source indices
-* [ ] {data-sources}, `read`, `manage`, and `index` index privileges on 
+* `Machine Learning: All` {kib} privileges
+* `Data Views Management: All` {kib} feature privileges
+* `read`, and `view_index_metadata` index privileges on your source indices
+* {data-sources} for your source indices
+* {data-sources}, `read`, `manage`, and `index` index privileges on 
   destination indices (for {dfanalytics-jobs} only)
 
 
 Within a {kib} space, for read-only access to the {ml-features}, you must have:
 
-[%interactive]
-* [ ] `Machine Learning: Read` {kib} privileges
-* [ ] {data-sources} for your source indices
-* [ ] `read` index privilege on your source indices
-* [ ] {data-sources} and `read` index privileges on destination indices (for 
+* `Machine Learning: Read` {kib} privileges
+* {data-sources} for your source indices
+* `read` index privilege on your source indices
+* {data-sources} and `read` index privileges on destination indices (for 
   {dfanalytics-jobs} only)
 
 IMPORTANT: A user who has full or read-only access to {ml-features} within
@@ -158,12 +158,11 @@ privileges and grant access to `machine_learning_admin` or
 Within a {kib} space, to upload and import files in the *{data-viz}*, you must 
 have:
 
-[%interactive]
-- [ ] `Machine Learning: Read` or `Discover: All` {kib} feature privileges
-- [ ] `Data Views Management: All` {kib} feature privileges
-- [ ] `ingest_admin` built-in role, or `manage_ingest_pipelines` cluster 
+- `Machine Learning: Read` or `Discover: All` {kib} feature privileges
+- `Data Views Management: All` {kib} feature privileges
+- `ingest_admin` built-in role, or `manage_ingest_pipelines` cluster 
   privilege
-- [ ] `create`, `create_index`, `manage` and `read` index privileges for
+- `create`, `create_index`, `manage` and `read` index privileges for
   destination indices
 
 For more information, see {ref}/security-privileges.html[Security privileges]