[7.x][DOCS] Updates ML links (#50387) (#50409)

docs/reference/intro.asciidoc

@@ -168,7 +168,7 @@ embroidery_ needles.
 ==== But wait, there’s more
 
 Want to automate the analysis of your time-series data? You can use
-{stack-ov}/ml-overview.html[machine learning] features to create accurate
+{ml-docs}/ml-overview.html[machine learning] features to create accurate
 baselines of normal behavior in your data and identify anomalous patterns. With
 machine learning, you can detect:
 

docs/reference/ml/anomaly-detection/apis/datafeedresource.asciidoc

@@ -9,7 +9,7 @@ A {dfeed} resource has the following properties:
   (object) If set, the {dfeed} performs aggregation searches.
   Support for aggregations is limited and should only be used with
   low cardinality data. For more information, see
-  {stack-ov}/ml-configuring-aggregation.html[Aggregating Data for Faster Performance].
+  {ml-docs}/ml-configuring-aggregation.html[Aggregating data for faster performance].
 
 `chunking_config`::
   (object) Specifies how data searches are split into time chunks.
@@ -53,7 +53,7 @@ A {dfeed} resource has the following properties:
   The <<ml-detectorconfig,detector configuration objects>> in a job can contain
   functions that use these script fields.
   For more information, see
-  {stack-ov}/ml-configuring-transform.html[Transforming Data With Script Fields].
+  {ml-docs}/ml-configuring-transform.html[Transforming data with script fields].
 
 `scroll_size`::
   (unsigned integer) The `size` parameter that is used in {es} searches.
@@ -104,7 +104,7 @@ an effort to determine whether any data has subsequently been added to the index
 If missing data is found, it is a good indication that the `query_delay` option
 is set too low and the data is being indexed after the {dfeed} has passed that
 moment in time. See 
-{stack-ov}/ml-delayed-data-detection.html[Working with delayed data].
+{ml-docs}/ml-delayed-data-detection.html[Working with delayed data].
 
 This check runs only on real-time {dfeeds}.
 

docs/reference/ml/anomaly-detection/apis/delete-filter.asciidoc

@@ -23,7 +23,7 @@ Deletes a filter.
 [[ml-delete-filter-desc]]
 ==== {api-description-title}
 
-This API deletes a {stack-ov}/ml-rules.html[filter]. 
+This API deletes a {ml-docs}/ml-rules.html[filter]. 
 If a {ml} job references the filter, you cannot delete the filter. You must 
 update or delete the job before you can delete the filter.
 

docs/reference/ml/anomaly-detection/apis/delete-forecast.asciidoc

@@ -35,7 +35,7 @@ one or more forecasts before they expire.
 NOTE: When you delete a job, its associated forecasts are deleted. 
 
 For more information, see
-{stack-ov}/ml-overview.html#ml-forecasting[Forecasting the future].
+{ml-docs}/ml-overview.html#ml-forecasting[Forecasting the future].
 
 [[ml-delete-forecast-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/eventresource.asciidoc

@@ -24,4 +24,4 @@ An events resource has the following properties:
  in milliseconds since the epoch or ISO 8601 format.
 
 For more information, see
-{stack-ov}/ml-calendars.html[Calendars and Scheduled Events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].

docs/reference/ml/anomaly-detection/apis/filterresource.asciidoc

@@ -14,4 +14,4 @@ A filter resource has the following properties:
 `items`::
   (array of strings) An array of strings which is the filter item list.
   
-For more information, see {stack-ov}/ml-rules.html[Machine learning custom rules].
+For more information, see {ml-docs}/ml-rules.html[Machine learning custom rules].

docs/reference/ml/anomaly-detection/apis/forecast.asciidoc

@@ -23,7 +23,7 @@ Predicts the future behavior of a time series by using its historical behavior.
 [[ml-forecast-desc]]
 ==== {api-description-title}
 
-See {stack-ov}/ml-overview.html#ml-forecasting[Forecasting the future].
+See {ml-docs}/ml-overview.html#ml-forecasting[Forecasting the future].
 
 [NOTE]
 ===============================

docs/reference/ml/anomaly-detection/apis/get-calendar-event.asciidoc

@@ -29,7 +29,7 @@ You can get scheduled event information for a single calendar or for all
 calendars by using `_all`.
 
 For more information, see
-{stack-ov}/ml-calendars.html[Calendars and scheduled events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].
 
 [[ml-get-calendar-event-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/get-calendar.asciidoc

@@ -29,7 +29,7 @@ You can get information for a single calendar or for all calendars by using
 `_all`.
 
 For more information, see 
-{stack-ov}/ml-calendars.html[Calendars and scheduled events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].
 
 [[ml-get-calendar-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/get-category.asciidoc

@@ -29,7 +29,7 @@ privileges. See <<security-privileges>> and
 ==== {api-description-title}
 
 For more information about categories, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 [[ml-get-category-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/get-filter.asciidoc

@@ -26,7 +26,7 @@ Retrieves filters.
 ==== {api-description-title}
 
 You can get a single filter or all filters. For more information, see 
-{stack-ov}/ml-rules.html[Machine learning custom rules].
+{ml-docs}/ml-rules.html[Machine learning custom rules].
 
 [[ml-get-filter-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/get-overall-buckets.asciidoc

@@ -36,7 +36,7 @@ by specifying `*` as the `<job_id>`.
 By default, an overall bucket has a span equal to the largest bucket span of the
 specified {anomaly-jobs}. To override that behavior, use the optional
 `bucket_span` parameter. To learn more about the concept of buckets, see
-{stack-ov}/ml-buckets.html[Buckets].
+{ml-docs}/ml-buckets.html[Buckets].
 
 The `overall_score` is calculated by combining the scores of all the buckets
 within the overall bucket span. First, the maximum `anomaly_score` per

docs/reference/ml/anomaly-detection/apis/jobresource.asciidoc

@@ -33,7 +33,7 @@ so do not set the `background_persist_interval` value too low.
 `custom_settings`::
   (object) Advanced configuration option. Contains custom meta data about the
   job. For example, it can contain custom URL information as shown in
-  {stack-ov}/ml-configuring-url.html[Adding Custom URLs to Machine Learning Results].
+  {ml-docs}/ml-configuring-url.html[Adding custom URLs to {ml} results].
 
 `data_description`::
   (object) Describes the data format and how APIs parse timestamp fields.
@@ -123,7 +123,7 @@ An analysis configuration object has the following properties:
   be categorized. The resulting categories must be used in a detector by setting
   `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword
   `mlcategory`. For more information, see
-  {stack-ov}/ml-configuring-categories.html[Categorizing Log Messages].
+  {ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 `categorization_filters`::
   (array of strings) If `categorization_field_name` is specified,
@@ -133,7 +133,7 @@ An analysis configuration object has the following properties:
   tune the categorization by excluding sequences from consideration when
   categories are defined. For example, you can exclude SQL statements that
   appear in your log files. For more information, see
-  {stack-ov}/ml-configuring-categories.html[Categorizing Log Messages].
+  {ml-docs}/ml-configuring-categories.html[Categorizing log messages].
   This property cannot be used at the same time as `categorization_analyzer`.
   If you only want to define simple regular expression filters that are applied
   prior to tokenization, setting this property is the easiest method.
@@ -256,14 +256,14 @@ NOTE: The `field_name` cannot contain double quotes or backslashes.
 `function`::
   (string) The analysis function that is used. 
   For example, `count`, `rare`, `mean`, `min`, `max`, and `sum`. For more
-  information, see {stack-ov}/ml-functions.html[Function Reference].
+  information, see {ml-docs}/ml-functions.html[Function reference].
 
 `over_field_name`::
   (string) The field used to split the data.
   In particular, this property is used for analyzing the splits with respect to
   the history of all splits. It is used for finding unusual values in the
   population of all splits. For more information, see
-  {stack-ov}/ml-configuring-pop.html[Performing population analysis].
+  {ml-docs}/ml-configuring-pop.html[Performing population analysis].
 
 `partition_field_name`::
   (string) The field used to segment the analysis.
@@ -419,13 +419,13 @@ the categorization analyzer produces then you find the original document that
 the categorization field value came from.
 
 For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 [float]
 [[ml-detector-custom-rule]]
 ==== Detector Custom Rule
 
-{stack-ov}/ml-rules.html[Custom rules] enable you to customize the way detectors 
+{ml-docs}/ml-rules.html[Custom rules] enable you to customize the way detectors 
 operate. 
 
 A custom rule has the following properties:
@@ -480,7 +480,7 @@ A condition has the following properties:
 
 A rule is required to either have a non-empty scope or at least one condition.
 For more examples see 
-{stack-ov}/ml-configuring-detector-custom-rules.html[Configuring Detector Custom Rules].
+{ml-docs}/ml-configuring-detector-custom-rules.html[Configuring detector custom rules].
 
 [float]
 [[ml-apilimits]]
@@ -502,7 +502,7 @@ The `analysis_limits` object has the following properties:
 --
 NOTE: The `categorization_examples_limit` only applies to analysis that uses categorization.
 For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 --
 

docs/reference/ml/anomaly-detection/apis/post-calendar-event.asciidoc

@@ -23,7 +23,7 @@ Posts scheduled events in a calendar.
 [[ml-post-calendar-event-desc]]
 ==== {api-description-title}
 
-This API accepts a list of {stack-ov}/ml-calendars.html[scheduled events], each
+This API accepts a list of {ml-docs}/ml-calendars.html[scheduled events], each
 of which must have a start time, end time, and description.
 
 [[ml-post-calendar-event-path-parms]]

docs/reference/ml/anomaly-detection/apis/put-calendar.asciidoc

@@ -24,7 +24,7 @@ Instantiates a calendar.
 ==== {api-description-title}
 
 For more information, see
-{stack-ov}/ml-calendars.html[Calendars and scheduled events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].
 
 [[ml-put-calendar-path-parms]]
 ==== {api-path-parms-title}

docs/reference/ml/anomaly-detection/apis/put-filter.asciidoc

@@ -23,7 +23,7 @@ Instantiates a filter.
 [[ml-put-filter-desc]]
 ==== {api-description-title}
 
-A {stack-ov}/ml-rules.html[filter] contains a list of strings. 
+A {ml-docs}/ml-rules.html[filter] contains a list of strings. 
 It can be used by one or more jobs. Specifically, filters are referenced in 
 the `custom_rules` property of <<ml-detectorconfig,detector configuration objects>>. 
 

docs/reference/ml/anomaly-detection/apis/resultsresource.asciidoc

@@ -38,7 +38,7 @@ Categorization results contain the definitions of _categories_ that have been
 identified. These are only applicable for jobs that are configured to analyze
 unstructured log data using categorization. These results do not contain a
 timestamp or any calculated scores. For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 * <<ml-results-buckets,Buckets>>
 * <<ml-results-influencers,Influencers>>

docs/reference/ml/ml-shared.asciidoc

@@ -175,7 +175,7 @@ at the same time as `categorization_filters`. The categorization analyzer
 specifies how the `categorization_field` is interpreted by the categorization
 process. The syntax is very similar to that used to define the `analyzer` in the
 <<indices-analyze,Analyze endpoint>>. For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 +
 --
 The `categorization_analyzer` field can be specified either as a string or as an
@@ -206,7 +206,7 @@ set this value to `0`, no examples are stored.
 --
 NOTE: The `categorization_examples_limit` only applies to analysis that uses
 categorization. For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 
 --
 end::categorization-examples-limit[]
@@ -216,7 +216,7 @@ If this property is specified, the values of the specified field will be
 categorized. The resulting categories must be used in a detector by setting
 `by_field_name`, `over_field_name`, or `partition_field_name` to the keyword
 `mlcategory`. For more information, see
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
 end::categorization-field-name[]
 
 tag::categorization-filters[]
@@ -226,7 +226,7 @@ are used to filter out matching sequences from the categorization field values.
 You can use this functionality to fine tune the categorization by excluding 
 sequences from consideration when categories are defined. For example, you can 
 exclude SQL statements that appear in your log files. For more information, see 
-{stack-ov}/ml-configuring-categories.html[Categorizing log messages]. This
+{ml-docs}/ml-configuring-categories.html[Categorizing log messages]. This
 property cannot be used at the same time as `categorization_analyzer`. If you
 only want to define simple regular expression filters that are applied prior to 
 tokenization, setting this property is the easiest method. If you also want to 
@@ -254,7 +254,7 @@ tag::custom-rules[]
 An array of custom rule objects, which enable you to customize the way detectors
 operate. For example, a rule may dictate to the detector conditions under which
 results should be skipped. For more examples, see 
-{stack-ov}/ml-configuring-detector-custom-rules.html[Configuring detector custom rules].
+{ml-docs}/ml-configuring-detector-custom-rules.html[Customizing detectors with custom rules].
 A custom rule has the following properties:
 +
 --
@@ -318,7 +318,7 @@ end::custom-rules[]
 tag::custom-settings[]
 Advanced configuration option. Contains custom meta data about the job. For
 example, it can contain custom URL information as shown in
-{stack-ov}/ml-configuring-url.html[Adding custom URLs to {ml} results].
+{ml-docs}/ml-configuring-url.html[Adding custom URLs to {ml} results].
 end::custom-settings[]
 
 tag::data-description[]
@@ -458,7 +458,7 @@ an effort to determine whether any data has subsequently been added to the index
 If missing data is found, it is a good indication that the `query_delay` option
 is set too low and the data is being indexed after the {dfeed} has passed that
 moment in time. See 
-{stack-ov}/ml-delayed-data-detection.html[Working with delayed data].
+{ml-docs}/ml-delayed-data-detection.html[Working with delayed data].
 
 This check runs only on real-time {dfeeds}.
 
@@ -631,7 +631,7 @@ end::from[]
 tag::function[]
 The analysis function that is used. For example, `count`, `rare`, `mean`, `min`, 
 `max`, and `sum`. For more information, see
-{stack-ov}/ml-functions.html[Function reference].
+{ml-docs}/ml-functions.html[Function reference].
 end::function[]
 
 tag::gamma[]
@@ -906,7 +906,7 @@ tag::over-field-name[]
 The field used to split the data. In particular, this property is used for 
 analyzing the splits with respect to the history of all splits. It is used for 
 finding unusual values in the population of all splits. For more information,
-see {stack-ov}/ml-configuring-pop.html[Performing population analysis].
+see {ml-docs}/ml-configuring-pop.html[Performing population analysis].
 end::over-field-name[]
 
 tag::outlier-fraction[]

docs/reference/modules/node.asciidoc

@@ -47,7 +47,7 @@ A node that has `xpack.ml.enabled` and `node.ml` set to `true`, which is the
 default behavior in the {es} {default-dist}. If you want to use {ml-features},
 there must be at least one {ml} node in your cluster. For more information about
 {ml-features}, see
-{stack-ov}/xpack-ml.html[Machine learning in the {stack}].
+{ml-docs}/xpack-ml.html[Machine learning in the {stack}].
 +
 IMPORTANT: If you use the {oss-dist}, do not set `node.ml`. Otherwise, the node
 fails to start.

docs/reference/redirects.asciidoc

@@ -611,19 +611,19 @@ See <<faster-prefix-queries>>.
 === Calendar resources
 
 See <<ml-get-calendar>> and
-{stack-ov}/ml-calendars.html[Calendars and scheduled events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].
 
 [role="exclude",id="ml-filter-resource"]
 === Filter resources
 
 See <<ml-get-filter>> and
-{stack-ov}/ml-rules.html[Machine learning custom rules].
+{ml-docs}/ml-rules.html[Machine learning custom rules].
 
 [role="exclude",id="ml-event-resource"]
 === Scheduled event resources
 
 See <<ml-get-calendar-event>> and
-{stack-ov}/ml-calendars.html[Calendars and scheduled events].
+{ml-docs}/ml-calendars.html[Calendars and scheduled events].
 
 [role="exclude",id="index-apis"]
 === Index APIs

docs/reference/release-notes/highlights-7.0.0.asciidoc

@@ -307,7 +307,7 @@ of you running JDK 11. In order to help new users from inadvertently running
 with low security, we’ve also dropped TLSv1.0 from our defaults. For those
 running older versions of Java, we have default options of TLSv1.2 and
 TLSv1.1. Have a look at our
-https://www.elastic.co/guide/en/elastic-stack-overview/7.0/ssl-tls.html[TLS
+https://www.elastic.co/guide/en/elasticsearch/reference/7.0/ssl-tls.html[TLS
 setup instructions] if you need help getting started.
 //end::notable-highlights[]
 

docs/reference/release-notes/highlights-7.1.0.asciidoc

@@ -31,7 +31,7 @@ and permissions for any user from the
 https://www.elastic.co/guide/en/elasticsearch/reference/7.1/configuring-file-realm.html[file realm]
 or the https://www.elastic.co/guide/en/elasticsearch/reference/7.1/configuring-native-realm.html[native realm]
 . Download https://www.elastic.co/downloads/elasticsearch[Elasticsearch],
-https://www.elastic.co/guide/en/elastic-stack-overview/7.1/authorization.html[configure RBAC],
+https://www.elastic.co/guide/en/elasticsearch/reference/7.1/authorization.html[configure RBAC],
 and run your cluster in production, knowing your private data stays private.
 Note that our advanced security features, such as single sign-on and Active Directory/LDAP
 authentication to field-level and document-level security, remain paid features.

docs/reference/release-notes/highlights-7.2.0.asciidoc

@@ -12,7 +12,7 @@
 ==== {dataframes-cap}
 
 beta[] You can now transform your data with
-{stack-ov}/ml-dataframes.html[data frames]. There is a new {kib} wizard that
+{ref}/transforms.html[data frames]. There is a new {kib} wizard that
 guides you through the process of creating a {dataframe-transform} to pivot and
 summarize your data and store it in a new index. Alternatively, you can use
 {ref}/data-frame-apis.html[{dataframe} APIs] to preview, create, and manage

docs/reference/release-notes/highlights-7.3.0.asciidoc

@@ -112,7 +112,7 @@ new {ref}/sql-index-frozen.html[`FROZEN`] keyword.
 [float]
 ==== Fixed memory leak when using templates in document-level security
 
-{stack-ov}/document-level-security.html[Document-level security] was using an
+{ref}/document-level-security.html[Document-level security] was using an
 unbounded cache for the set of visible documents. This could lead to a memory
 leak when using a templated query as a role query. The cache has been fixed to
 evict based on memory usage and has a limit of 50MB.
@@ -138,7 +138,7 @@ similar level as what you could have on pre-6.0 releases.
 [[release-highlights-7.3.0-transforms]]
 ==== {dataframes-cap}: transform and pivot your streaming data
 
-beta[] {stack-ov}/ml-dataframes.html[{dataframe-transforms-cap}] are a core new
+beta[] {ref}/transforms.html[{dataframe-transforms-cap}] are a core new
 feature in {es} that enable you to transform an existing index to a secondary,
 summarized index. {dataframe-transforms-cap} enable you to pivot your data and
 create entity-centric indices that can summarize the behavior of an entity. This
@@ -159,7 +159,7 @@ visualizations and custom types of analysis.
 [[release-highlights-7.3.0-outlier-detection]]
 ==== Discover your most unusual data using {oldetection}
 
-The goal of {stack-ov}/dfa-outlier-detection.html[{oldetection}] is to find 
+The goal of {ml-docs}/dfa-outlier-detection.html[{oldetection}] is to find 
 the most unusual data points in an index. We analyse the numerical fields of 
 each data point (document in an index) and annotate them with how unusual they 
 are.

docs/reference/release-notes/highlights-7.4.0.asciidoc

@@ -85,7 +85,7 @@ or you need to restore {es} data.
 [float]
 ==== API key management
 
-New {stack-ov}/security-privileges.html[cluster privileges] to manage API keys have been added,
+New {ref}/security-privileges.html[cluster privileges] to manage API keys have been added,
 allowing cluster administrators to manage everything,
 and regular users to manage their own keys.
 Users can create API keys
@@ -133,7 +133,7 @@ facilitates notarization of {es} for continued support on macOS.
 [float]
 ==== Regression analysis - Experimental
 
-{stack-ov}/dfa-regression.html[Regression analysis] is an experimental machine learning process
+{ml-docs}/dfa-regression.html[Regression analysis] is an experimental machine learning process
 for estimating the relationships among a number of feature variables and a dependent variable,
 then making further predictions based on the described relationship.
 

docs/reference/release-notes/highlights-7.5.0.asciidoc

@@ -53,7 +53,7 @@ pause auto-follow patterns.
 [float]
 ==== {ml-cap} {classanalysis}
 
-{stack-ov}/dfa-classification.html[{classanalysis-cap}] is a supervised {ml}
+{ml-docs}/dfa-classification.html[{classanalysis-cap}] is a supervised {ml}
 process for predicting a class or category of a given data point in a dataset.
 For example, it can determine whether an email is spam or not.
 {classification-cap} is for predicting discrete, categorical values, unlike

docs/reference/setup/restart-cluster.asciidoc

@@ -55,7 +55,7 @@ was automatically saved. This option avoids the overhead of managing active jobs
 during the shutdown and is faster than explicitly stopping {dfeeds} and closing 
 jobs.
 
-* {stack-ov}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
+* {ml-docs}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
 saves the model state at the time of closure. When you reopen the jobs after the
 cluster restart, they use the exact same model. However, saving the latest model 
 state takes longer than using upgrade mode, especially if you have a lot of jobs 

docs/reference/setup/setup-xclient.asciidoc

@@ -113,4 +113,4 @@ Then in your project's `pom.xml` if using maven, add the following repositories
 --
 
 . If you are using {stack} {security-features}, there are more configuration
-steps. See {stack-ov}/java-clients.html[Java Client and Security].
+steps. See {ref}/java-clients.html[Java Client and security].

docs/reference/upgrade/close-ml.asciidoc

@@ -34,7 +34,7 @@ state that was automatically saved. This option avoids the overhead of managing
 active jobs during the upgrade and is faster than explicitly stopping {dfeeds}
 and closing jobs.
 
-* {stack-ov}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
+* {ml-docs}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
 saves the model state at the time of closure. When you reopen the jobs after the
 upgrade, they use the exact same model. However, saving the latest model state
 takes longer than using upgrade mode, especially if you have a lot of jobs or

docs/reference/upgrade/reindex_upgrade.asciidoc

@@ -62,7 +62,7 @@ If you use {ml-features} and your {ml} indices were created before
 {prev-major-version}, you must temporarily halt the tasks associated with your
 {ml} jobs and {dfeeds} and prevent new jobs from opening during the reindex. Use
 the <<ml-set-upgrade-mode,set upgrade mode API>> or
-{stack-ov}/stopping-ml.html[stop all {dfeeds} and close all {ml} jobs].
+{ml-docs}/stopping-ml.html[stop all {dfeeds} and close all {ml} jobs].
 
 If you use {es} {security-features}, before you reindex `.security*` internal
 indices it is a good idea to create a temporary superuser account in the `file`
@@ -121,7 +121,7 @@ from a 6.6 or later cluster, it is a good idea to temporarily halt the tasks
 associated with your {ml} jobs and {dfeeds} to prevent inconsistencies between
 different {ml} indices that are reindexed at slightly different times. Use the
 <<ml-set-upgrade-mode,set upgrade mode API>> or 
-{stack-ov}/stopping-ml.html[stop all {dfeeds} and close all {ml} jobs].
+{ml-docs}/stopping-ml.html[stop all {dfeeds} and close all {ml} jobs].
 endif::include-xpack[]
 
 =============================================

x-pack/docs/en/rest-api/security/create-role-mappings.asciidoc

@@ -27,7 +27,7 @@ Role mappings define which roles are assigned to each user. Each mapping has
 _rules_ that identify users and a list of _roles_ that are granted to those users.
 
 The role mapping APIs are generally the preferred way to manage role mappings
-rather than using {stack-ov}/mapping-roles.html#mapping-roles-file[role mapping files].
+rather than using {ref}/mapping-roles.html#mapping-roles-file[role mapping files].
 The create or update role mappings API cannot update role mappings that are defined
 in role mapping files.
 

x-pack/docs/en/security/securing-communications/separating-node-client-traffic.asciidoc

@@ -40,7 +40,7 @@ transport.profiles.client.bind_host: 1.1.1.1 <2>
 <2> The bind address for the network used for client communication
 
 If separate networks are not available, then
-{stack-ov}/ip-filtering.html[IP Filtering] can
+{ref}/ip-filtering.html[IP Filtering] can
 be enabled to limit access to the profiles.
 
 When using SSL for transport, a different set of certificates can also be used
@@ -68,4 +68,4 @@ transport.profiles.client.xpack.security.ssl.client_authentication: none
 This setting keeps certificate authentication active for node-to-node traffic,
 but removes the requirement to distribute a signed certificate to transport
 clients. For more information, see
-{stack-ov}/java-clients.html#transport-client[Configuring the Transport Client to work with a Secured Cluster].
+{ref}/java-clients.html#transport-client[Configuring the Transport Client to work with a Secured Cluster].