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[DOCS] Updates ML links (#50387)
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docs/reference/intro.asciidoc

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

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[[ml-delete-filter-desc]]
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==== {api-description-title}
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This API deletes a {stack-ov}/ml-rules.html[filter].
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This API deletes a {ml-docs}/ml-rules.html[filter].
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If a {ml} job references the filter, you cannot delete the filter. You must
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update or delete the job before you can delete the filter.
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docs/reference/ml/anomaly-detection/apis/delete-forecast.asciidoc

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

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

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in milliseconds since the epoch or ISO 8601 format.
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For more information, see
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{stack-ov}/ml-calendars.html[Calendars and Scheduled Events].
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{ml-docs}/ml-calendars.html[Calendars and scheduled events].

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

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

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

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

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

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calendars by using `_all`.
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For more information, see
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{stack-ov}/ml-calendars.html[Calendars and scheduled events].
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{ml-docs}/ml-calendars.html[Calendars and scheduled events].
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[[ml-get-calendar-event-path-parms]]
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==== {api-path-parms-title}

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

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`_all`.
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For more information, see
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{stack-ov}/ml-calendars.html[Calendars and scheduled events].
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{ml-docs}/ml-calendars.html[Calendars and scheduled events].
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[[ml-get-calendar-path-parms]]
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==== {api-path-parms-title}

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

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@@ -37,7 +37,7 @@ The anomaly results from a categorization analysis are available as bucket,
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influencer, and record results. For example, the results might indicate that
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at 16:45 there was an unusual count of log message category 11. You can then
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examine the description and examples of that category. For more information, see
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{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
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[[ml-get-category-path-parms]]
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==== {api-path-parms-title}

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

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

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

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

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

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

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

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

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

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

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If set, the {dfeed} performs aggregation searches. Support for aggregations is
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limited and should only be used with low cardinality data. For more information,
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see
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{stack-ov}/ml-configuring-aggregation.html[Aggregating data for faster performance].
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{ml-docs}/ml-configuring-aggregation.html[Aggregating data for faster performance].
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end::aggregations[]
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tag::allow-lazy-open[]
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specifies how the `categorization_field` is interpreted by the categorization
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process. The syntax is very similar to that used to define the `analyzer` in the
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<<indices-analyze,Analyze endpoint>>. For more information, see
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{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
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--
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The `categorization_analyzer` field can be specified either as a string or as an
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--
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NOTE: The `categorization_examples_limit` only applies to analysis that uses
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categorization. For more information, see
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{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
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end::categorization-examples-limit[]
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categorized. The resulting categories must be used in a detector by setting
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`by_field_name`, `over_field_name`, or `partition_field_name` to the keyword
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`mlcategory`. For more information, see
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{stack-ov}/ml-configuring-categories.html[Categorizing log messages].
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages].
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end::categorization-field-name[]
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tag::categorization-filters[]
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You can use this functionality to fine tune the categorization by excluding
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sequences from consideration when categories are defined. For example, you can
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exclude SQL statements that appear in your log files. For more information, see
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{stack-ov}/ml-configuring-categories.html[Categorizing log messages]. This
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{ml-docs}/ml-configuring-categories.html[Categorizing log messages]. This
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property cannot be used at the same time as `categorization_analyzer`. If you
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only want to define simple regular expression filters that are applied prior to
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tokenization, setting this property is the easiest method. If you also want to
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An array of custom rule objects, which enable you to customize the way detectors
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{stack-ov}/ml-configuring-detector-custom-rules.html[Configuring detector custom rules].
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{ml-docs}/ml-configuring-detector-custom-rules.html[Customizing detectors with custom rules].
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A custom rule has the following properties:
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tag::custom-settings[]
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Advanced configuration option. Contains custom meta data about the job. For
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{stack-ov}/ml-configuring-url.html[Adding custom URLs to {ml} results].
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{ml-docs}/ml-configuring-url.html[Adding custom URLs to {ml} results].
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end::custom-settings[]
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tag::data-description[]
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If missing data is found, it is a good indication that the `query_delay` option
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is set too low and the data is being indexed after the {dfeed} has passed that
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moment in time. See
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{stack-ov}/ml-delayed-data-detection.html[Working with delayed data].
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{ml-docs}/ml-delayed-data-detection.html[Working with delayed data].
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This check runs only on real-time {dfeeds}.
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tag::function[]
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The analysis function that is used. For example, `count`, `rare`, `mean`, `min`,
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`max`, and `sum`. For more information, see
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{stack-ov}/ml-functions.html[Function reference].
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{ml-docs}/ml-functions.html[Function reference].
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end::function[]
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tag::gamma[]
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see {stack-ov}/ml-configuring-pop.html[Performing population analysis].
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see {ml-docs}/ml-configuring-pop.html[Performing population analysis].
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tag::outlier-fraction[]
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{stack-ov}/ml-configuring-transform.html[Transforming data with script fields]
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{ml-docs}/ml-configuring-transform.html[Transforming data with script fields]
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and <<request-body-search-script-fields,Script fields>>.
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end::script-fields[]
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docs/reference/modules/node.asciidoc

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default behavior in the {es} {default-dist}. If you want to use {ml-features},
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{ml-features}, see
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{stack-ov}/xpack-ml.html[Machine learning in the {stack}].
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{ml-docs}/xpack-ml.html[Machine learning in the {stack}].
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IMPORTANT: If you use the {oss-dist}, do not set `node.ml`. Otherwise, the node
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docs/reference/redirects.asciidoc

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

docs/reference/setup/restart-cluster.asciidoc

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

docs/reference/upgrade/close-ml.asciidoc

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* {stack-ov}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
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* {ml-docs}/stopping-ml.html[Stop all {dfeeds} and close all jobs]. This option
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saves the model state at the time of closure. When you reopen the jobs after the
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upgrade, they use the exact same model. However, saving the latest model state
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takes longer than using upgrade mode, especially if you have a lot of jobs or

docs/reference/upgrade/reindex_upgrade.asciidoc

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

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