[DOCS] Cleans up xpackml attributes

docs/reference/ml/aggregations.asciidoc

@@ -8,7 +8,7 @@ and to configure your jobs to analyze aggregated data.
 
 One of the benefits of aggregating data this way is that {es} automatically
 distributes these calculations across your cluster. You can then feed this
-aggregated data into {xpackml} instead of raw results, which
+aggregated data into the {ml-features} instead of raw results, which
 reduces the volume of data that must be considered while detecting anomalies.
 
 There are some limitations to using aggregations in {dfeeds}, however.

docs/reference/ml/apis/resultsresource.asciidoc

@@ -269,7 +269,7 @@ probability of this occurrence.
 
 There can be many anomaly records depending on the characteristics and size of
 the input data. In practice, there are often too many to be able to manually
-process them. The {xpackml} features therefore perform a sophisticated
+process them. The {ml-features} therefore perform a sophisticated
 aggregation of the anomaly records into buckets.
 
 The number of record results depends on the number of anomalies found in each

docs/reference/ml/configuring.asciidoc

@@ -2,12 +2,12 @@
 [[ml-configuring]]
 == Configuring machine learning
 
-If you want to use {xpackml} features, there must be at least one {ml} node in
+If you want to use {ml-features}, there must be at least one {ml} node in
 your cluster and all master-eligible nodes must have {ml} enabled. By default,
 all nodes are {ml} nodes. For more information about these settings, see 
 {ref}/modules-node.html#modules-node-xpack[{ml} nodes].
 
-To use the {xpackml} features to analyze your data, you must create a job and
+To use the {ml-features} to analyze your data, you must create a job and
 send your data to that job.
 
 * If your data is stored in {es}:

docs/reference/ml/functions.asciidoc

@@ -2,7 +2,7 @@
 [[ml-functions]]
 == Function reference
 
-The {xpackml} features include analysis functions that provide a wide variety of
+The {ml-features} include analysis functions that provide a wide variety of
 flexible ways to analyze data for anomalies.
 
 When you create jobs, you specify one or more detectors, which define the type of

docs/reference/ml/functions/count.asciidoc

@@ -14,7 +14,7 @@ in one field is unusual, as opposed to the total count.
 Use high-sided functions if you want to monitor unusually high event rates.
 Use low-sided functions if you want to look at drops in event rate.
 
-The {xpackml} features include the following count functions:
+The {ml-features} include the following count functions:
 
 * xref:ml-count[`count`, `high_count`, `low_count`]
 * xref:ml-nonzero-count[`non_zero_count`, `high_non_zero_count`, `low_non_zero_count`]

docs/reference/ml/functions/geo.asciidoc

@@ -5,7 +5,7 @@
 The geographic functions detect anomalies in the geographic location of the
 input data.
 
-The {xpackml} features include the following geographic function: `lat_long`.
+The {ml-features} include the following geographic function: `lat_long`.
 
 NOTE: You cannot create forecasts for jobs that contain geographic functions. 
 You also cannot add rules with conditions to detectors that use geographic 
@@ -72,7 +72,7 @@ For example, JSON data might contain the following transaction coordinates:
 
 In {es}, location data is likely to be stored in `geo_point` fields. For more
 information, see {ref}/geo-point.html[Geo-point datatype]. This data type is not
-supported natively in {xpackml} features. You can, however, use Painless scripts
+supported natively in {ml-features}. You can, however, use Painless scripts
 in `script_fields` in your {dfeed} to transform the data into an appropriate
 format. For example, the following Painless script transforms
 `"coords": {"lat" : 41.44, "lon":90.5}` into `"lat-lon": "41.44,90.5"`:

docs/reference/ml/functions/info.asciidoc

@@ -6,7 +6,7 @@ that is contained in strings within a bucket. These functions can be used as
 a more sophisticated method to identify incidences of data exfiltration or
 C2C activity, when analyzing the size in bytes of the data might not be sufficient.
 
-The {xpackml} features include the following information content functions:
+The {ml-features} include the following information content functions:
 
 * `info_content`, `high_info_content`, `low_info_content`
 

docs/reference/ml/functions/metric.asciidoc

@@ -6,7 +6,7 @@ The metric functions include functions such as mean, min and max. These values
 are calculated for each bucket. Field values that cannot be converted to
 double precision floating point numbers are ignored.
 
-The {xpackml} features include the following metric functions:
+The {ml-features} include the following metric functions:
 
 * <<ml-metric-min,`min`>>
 * <<ml-metric-max,`max`>>

docs/reference/ml/functions/rare.asciidoc

@@ -27,7 +27,7 @@ with shorter bucket spans typically being measured in minutes, not hours.
 for typical data.
 ====
 
-The {xpackml} features include the following rare functions:
+The {ml-features} include the following rare functions:
 
 * <<ml-rare,`rare`>>
 * <<ml-freq-rare,`freq_rare`>>
@@ -85,7 +85,7 @@ different rare status codes compared to the population is regarded as highly
 anomalous. This analysis is based on the number of different status code values,
 not the count of occurrences.
 
-NOTE: To define a status code as rare the {xpackml} features look at the number
+NOTE: To define a status code as rare the {ml-features} look at the number
 of distinct status codes that occur, not the number of times the status code
 occurs. If a single client IP experiences a single unique status code, this
 is rare, even if it occurs for that client IP in every bucket.

docs/reference/ml/functions/sum.asciidoc

@@ -11,7 +11,7 @@ If want to look at drops in totals, use low-sided functions.
 If your data is sparse, use `non_null_sum` functions. Buckets without values are
 ignored; buckets with a zero value are analyzed.
 
-The {xpackml} features include the following sum functions:
+The {ml-features} include the following sum functions:
 
 * xref:ml-sum[`sum`, `high_sum`, `low_sum`]
 * xref:ml-nonnull-sum[`non_null_sum`, `high_non_null_sum`, `low_non_null_sum`]

docs/reference/ml/functions/time.asciidoc

@@ -6,7 +6,7 @@ The time functions detect events that happen at unusual times, either of the day
 or of the week. These functions can be used to find unusual patterns of behavior,
 typically associated with suspicious user activity.
 
-The {xpackml} features include the following time functions:
+The {ml-features} include the following time functions:
 
 * <<ml-time-of-day,`time_of_day`>>
 * <<ml-time-of-week,`time_of_week`>>

docs/reference/ml/transforms.asciidoc

@@ -569,7 +569,7 @@ GET _ml/datafeeds/datafeed-test4/_preview
 // TEST[skip:needs-licence]
 
 In {es}, location data can be stored in `geo_point` fields but this data type is
-not supported natively in {xpackml} analytics. This example of a script field
+not supported natively in {ml} analytics. This example of a script field
 transforms the data into an appropriate format. For more information,
 see <<ml-geo-functions>>.
 

docs/reference/modules/ml-node.asciidoc

@@ -9,10 +9,9 @@ If {xpack} is installed, there is an additional node type:
 <<ml-node,Machine learning node>>::
 
 A node that has `xpack.ml.enabled` and `node.ml` set to `true`, which is the
-default behavior when {xpack} is installed. If you want to use {xpackml}
-features, there must be at least one {ml} node in your cluster. For more
-information about {xpackml} features,
-see {xpack-ref}/xpack-ml.html[Machine Learning in the Elastic Stack].
+default behavior when {xpack} is installed. 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}].
 
 IMPORTANT: Do not set use the `node.ml` setting unless {xpack} is installed.
 Otherwise, the node fails to start.
@@ -88,11 +87,11 @@ node.ml: false <5>
 [[ml-node]]
 === [xpack]#Machine learning node#
 
-The {xpackml} features provide {ml} nodes, which run jobs and handle {ml} API
+The {ml-features} provide {ml} nodes, which run jobs and handle {ml} API
 requests. If `xpack.ml.enabled` is set to true and `node.ml` is set to `false`,
 the node can service API requests but it cannot run jobs.
 
-If you want to use {xpackml} features in your cluster, you must enable {ml}
+If you want to use {ml-features} in your cluster, you must enable {ml}
 (set `xpack.ml.enabled` to `true`) on all master-eligible nodes. Do not use
 these settings if you do not have {xpack} installed.