[DOCS] Updates ML/anomaly detection terms in the Kibana guide

docs/ml/creating-df-kib.asciidoc

@@ -1,3 +1,4 @@
+[role="xpack"]
 [[creating-df-kib]]
 == Creating {dataframe-transforms}
 

docs/ml/creating-jobs.asciidoc

@@ -1,8 +1,8 @@
 [role="xpack"]
 [[ml-jobs]]
-== Creating machine learning jobs
+== Creating {anomaly-jobs}
 
-Machine learning jobs contain the configuration information and metadata
+{anomaly-jobs-cap} contain the configuration information and metadata
 necessary to perform an analytics task.
 
 {kib} provides the following wizards to make it easier to create jobs:
@@ -33,7 +33,7 @@ appears:
 [role="screenshot"]
 image::ml/images/ml-data-recognizer-sample.jpg[A screenshot of the {kib} sample data web log job creation wizard]
 
-TIP: Alternatively, after you load a sample data set on the {kib} home page, you can click *View data* > *ML jobs*. There are {ml} jobs for both the sample eCommerce orders data set and the sample web logs data set.
+TIP: Alternatively, after you load a sample data set on the {kib} home page, you can click *View data* > *ML jobs*. There are {anomaly-jobs} for both the sample eCommerce orders data set and the sample web logs data set.
 
 If you use {filebeat-ref}/index.html[{filebeat}]
 to ship access logs from your
@@ -57,17 +57,17 @@ wizards appear:
 [role="screenshot"]
 image::ml/images/ml-data-recognizer-metricbeat.jpg[A screenshot of the {metricbeat} job creation wizards]
 
-These wizards create {ml} jobs, dashboards, searches, and visualizations that
-are customized to help you analyze your {auditbeat}, {filebeat}, and
+These wizards create {anomaly-jobs}, dashboards, searches, and visualizations 
+that are customized to help you analyze your {auditbeat}, {filebeat}, and
 {metricbeat} data.
 
 [NOTE] 
 ===============================
 If your data is located outside of {es}, you cannot use {kib} to create
 your jobs and you cannot use {dfeeds} to retrieve your data in real time.
-Machine learning analysis is still possible, however, by using APIs to
+{anomal-detect-cap} is still possible, however, by using APIs to
 create and manage jobs and post data to them. For more information, see
-{ref}/ml-apis.html[Machine Learning APIs].
+{ref}/ml-apis.html[{ml-cap} {anomaly-detect} APIs].
 ===============================
 
 ////

docs/ml/index.asciidoc

@@ -1,35 +1,36 @@
 [role="xpack"]
 [[xpack-ml]]
-= Machine Learning
+= {ml-cap}
 
 [partintro]
 --
 
 As datasets increase in size and complexity, the human effort required to
 inspect dashboards or maintain rules for spotting infrastructure problems,
-cyber attacks, or business issues becomes impractical. The Elastic {ml-features}
-automatically model the normal behavior of your time series data — learning
-trends, periodicity, and more — in real time to identify anomalies, streamline
-root cause analysis, and reduce false positives.
+cyber attacks, or business issues becomes impractical. The Elastic {ml} 
+{anomaly-detect} feature automatically models the normal behavior of your time 
+series data — learning trends, periodicity, and more — in real time to identify 
+anomalies, streamline root cause analysis, and reduce false positives.
 
-The {ml-features} run in and scale with {es}, and include an
-intuitive UI on the {kib} *Machine Learning* page for creating anomaly detection
-jobs and understanding results.
+{anomaly-detect-cap} runs in and scales with {es}, and includes an
+intuitive UI on the {kib} *Machine Learning* page for creating {anomaly-jobs} 
+and understanding results.
 
 If you have a basic license, you can use the *Data Visualizer* to learn more
 about your data. In particular, if your data is stored in {es} and contains a
 time field, you can use the *Data Visualizer* to identify possible fields for
-{ml} analysis:
+{anomaly-detect}:
 
 [role="screenshot"]
 image::ml/images/ml-data-visualizer-sample.jpg[Data Visualizer for sample flight data]
 
-experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in size). 
-The {ml-features} identify the file format and field mappings. You can then 
-optionally import that data into an {es} index.  
+experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in 
+size). The *Data Visualizer* identifies the file format and field mappings. You 
+can then optionally import that data into an {es} index.  
 
-If you have a trial or platinum license, you can <<ml-jobs,create {ml} jobs>> 
-and manage jobs and {dfeeds} from the *Job Management* pane: 
+If you have a trial or platinum license, you can 
+<<ml-jobs,create {anomaly-jobs}>> and manage jobs and {dfeeds} from the *Job 
+Management* pane: 
 
 [role="screenshot"]
 image::ml/images/ml-job-management.jpg[Job Management]
@@ -42,7 +43,7 @@ You can use the *Settings* pane to create and edit
 image::ml/images/ml-settings.jpg[Calendar Management]
 
 The *Anomaly Explorer* and *Single Metric Viewer* display the results of your
-{ml} jobs. For example:
+{anomaly-jobs}. For example:
 
 [role="screenshot"]
 image::ml/images/ml-single-metric-viewer.jpg[Single Metric Viewer]
@@ -56,17 +57,17 @@ occurring in your operational environment at that time:
 image::ml/images/ml-annotations-list.jpg[Single Metric Viewer with annotations]
 
 In some circumstances, annotations are also added automatically. For example, if
-the {ml} analytics detect that there is missing data, it annotates the affected
+the {anomaly-job} detects that there is missing data, it annotates the affected 
 time period. For more information, see 
-{stack-ov}/ml-delayed-data-detection.html[Handling delayed data].
-The *Job Management* pane shows the full list of annotations for each job.
+{stack-ov}/ml-delayed-data-detection.html[Handling delayed data]. The 
+*Job Management* pane shows the full list of annotations for each job.
 
-NOTE: The {kib} {ml-features} use pop-ups. You must configure your
-web browser so that it does not block pop-up windows or create an exception for
-your {kib} URL.
+NOTE: The {kib} {ml-features} use pop-ups. You must configure your web 
+browser so that it does not block pop-up windows or create an exception for your 
+{kib} URL.
 
-For more information about {ml}, see
-{stack-ov}/xpack-ml.html[Machine learning in the {stack}].
+For more information about the {anomaly-detect} feature, see
+{stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}].
 
 --
 

docs/ml/job-tips.asciidoc

@@ -5,24 +5,25 @@
 <titleabbrev>Job tips</titleabbrev>
 ++++
 
-When you are creating a job in {kib}, the job creation wizards can provide
-advice based on the characteristics of your data. By heeding these suggestions,
-you can create jobs that are more likely to produce insightful {ml} results.
+When you create an {anomaly-job} in {kib}, the job creation wizards can
+provide advice based on the characteristics of your data. By heeding these
+suggestions, you can create jobs that are more likely to produce insightful {ml}
+results.
 
 [[bucket-span]]
 ==== Bucket span
 
 The bucket span is the time interval that {ml} analytics use to summarize and
-model data for your job. When you create a job in {kib}, you can choose to
-estimate a bucket span value based on your data characteristics. 
+model data for your job. When you create an {anomaly-job} in {kib}, you can
+choose to estimate a bucket span value based on your data characteristics. 
 
 NOTE: The bucket span must contain a valid time interval. For more information, 
 see {ref}/ml-job-resource.html#ml-analysisconfig[Analysis configuration objects].
 
 If you choose a value that is larger than one day or is significantly different 
 than the estimated value, you receive an informational message. For more 
 information about choosing an appropriate bucket span, see 
-{xpack-ref}/ml-buckets.html[Buckets].
+{stack-ov}/ml-buckets.html[Buckets].
 
 [[cardinality]]
 ==== Cardinality
@@ -40,14 +41,14 @@ job uses more memory resources. In particular, if the cardinality of the
 Likewise if you are performing population analysis and the cardinality of the
 `over_field_name` is below 10, you are advised that this might not be a suitable
 field to use. For more information, see
-{xpack-ref}/ml-configuring-pop.html[Performing Population Analysis].
+{stack-ov}/ml-configuring-pop.html[Performing Population Analysis].
 
 [[detectors]]
 ==== Detectors
 
-Each job must have one or more _detectors_. A detector applies an analytical 
-function to specific fields in your data. If your job does not contain a 
-detector or the detector does not contain a 
+Each {anomaly-job} must have one or more _detectors_. A detector applies an
+analytical function to specific fields in your data. If your job does not
+contain a  detector or the detector does not contain a 
 {stack-ov}/ml-functions.html[valid function], you receive an error.
 
 If a job contains duplicate detectors, you also receive an error. Detectors are 
@@ -57,9 +58,9 @@ duplicates if they have the same `function`, `field_name`, `by_field_name`,
 [[influencers]]
 ==== Influencers
 
-When you create a job, you can specify _influencers_, which are also sometimes
-referred to as _key fields_. Picking an influencer is strongly recommended for
-the following reasons:
+When you create an {anomaly-job}, you can specify _influencers_, which are also 
+sometimes referred to as _key fields_. Picking an influencer is strongly
+recommended for the following reasons:
 
 * It allows you to more easily assign blame for the anomaly
 * It simplifies and aggregates the results
@@ -78,11 +79,11 @@ The job creation wizards in {kib} can suggest which fields to use as influencers
 [[model-memory-limits]]
 ==== Model memory limits
 
-For each job, you can optionally specify a `model_memory_limit`, which is the 
-approximate maximum amount of memory resources that are required for analytical 
-processing. The default value is 1 GB. Once this limit is approached, data 
-pruning becomes more aggressive. Upon exceeding this limit, new entities are not 
-modeled. 
+For each {anomaly-job}, you can optionally specify a `model_memory_limit`, which
+is the approximate maximum amount of memory resources that are required for
+analytical processing. The default value is 1 GB. Once this limit is approached,
+data pruning becomes more aggressive. Upon exceeding this limit, new entities
+are not modeled. 
 
 You can also optionally specify the `xpack.ml.max_model_memory_limit` setting. 
 By default, it's not set, which means there is no upper bound on the acceptable 
@@ -92,9 +93,9 @@ TIP: If you set the `model_memory_limit` too high, it will be impossible to open
 the job; jobs cannot be allocated to nodes that have insufficient memory to run 
 them.
 
-If the estimated model memory limit for a job is greater than the model memory 
-limit for the job or the maximum model memory limit for the cluster, the job 
-creation wizards in {kib} generate a warning. If the estimated memory 
+If the estimated model memory limit for an {anomaly-job} is greater than the
+model memory limit for the job or the maximum model memory limit for the cluster,
+the job creation wizards in {kib} generate a warning. If the estimated memory 
 requirement is only a little higher than the `model_memory_limit`, the job will 
 probably produce useful results. Otherwise, the actions you take to address 
 these warnings vary depending on the resources available in your cluster: