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[DOCS] Changes level offset for anomaly detection pages #1292

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[DOCS] Changes level offset for anomaly detection pages #1292

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[DOCS] Changes level offset for anomaly detection docs
lcawl Jul 20, 2020
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[DOCS] Temporary omit elasticsearch inclusions
lcawl Jul 20, 2020
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6 changes: 3 additions & 3 deletions docs/en/stack/ml/anomaly-detection/anomaly-examples.asciidoc
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[role="xpack"]
[testenv="platinum"]
[[anomaly-examples]]
== {anomaly-detect-cap} examples
= {anomaly-detect-cap} examples
++++
<titleabbrev>Examples</titleabbrev>
++++
Expand All @@ -15,13 +15,13 @@ The scenarios in this section describe some best practices for generating useful
* <<ml-configuring-aggregation>>
* <<ml-configuring-categories>>
* <<ml-configuring-detector-custom-rules>>
* <<ml-configuring-pop>>
* <<ml-configuring-populations>>
* <<ml-configuring-transform>>
* <<ml-delayed-data-detection>>

[discrete]
[[anomaly-examples-blog-posts]]
=== {anomaly-detect-cap} examples in blog posts
== {anomaly-detect-cap} examples in blog posts

The blog posts listed below show how to get the most out of Elastic {ml}
{anomaly-detect}.
Expand Down
6 changes: 3 additions & 3 deletions docs/en/stack/ml/anomaly-detection/create-jobs.asciidoc
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[role="xpack"]
[[create-jobs]]
== Create {anomaly-jobs}
= Create {anomaly-jobs}

{anomaly-jobs-cap} contain the configuration information and metadata
necessary to perform an analytics task.
Expand All @@ -21,7 +21,7 @@ A _multi-metric job_ can contain more than one detector, which is more efficient
than running multiple jobs against the same data.

A _population job_ detects activity that is unusual compared to the behavior of
the population. For more information, see <<ml-configuring-pop>>.
the population. For more information, see <<ml-configuring-populations>>.

A _categorization job_ groups log messages into categories and uses
<<ml-count-functions,count>> or <<ml-rare-functions,rare>> functions to detect
Expand Down Expand Up @@ -73,7 +73,7 @@ These wizards create {anomaly-jobs}, dashboards, searches, and visualizations
that are customized to help you analyze your {auditbeat}, {filebeat}, and
{metricbeat} data.

[NOTE]
[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.
Expand Down
82 changes: 48 additions & 34 deletions docs/en/stack/ml/anomaly-detection/index.asciidoc
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include::xpack-ml.asciidoc[]

include::ml-overview.asciidoc[]
include::ml-overview.asciidoc[leveloffset=+1]

include::ml-concepts.asciidoc[]
include::ml-concepts.asciidoc[leveloffset=+1]

include::ml-jobs.asciidoc[leveloffset=+1]
include::ml-jobs.asciidoc[leveloffset=+2]

include::ml-datafeeds.asciidoc[leveloffset=+1]
include::ml-datafeeds.asciidoc[leveloffset=+2]

include::ml-buckets.asciidoc[leveloffset=+1]
include::ml-buckets.asciidoc[leveloffset=+2]

include::ml-influencers.asciidoc[leveloffset=+1]
include::ml-influencers.asciidoc[leveloffset=+2]

include::ml-calendars.asciidoc[leveloffset=+1]
include::ml-calendars.asciidoc[leveloffset=+2]

include::ml-rules.asciidoc[leveloffset=+1]
include::ml-rules.asciidoc[leveloffset=+2]

include::ml-model-snapshots.asciidoc[leveloffset=+1]
include::ml-model-snapshots.asciidoc[leveloffset=+2]

include::ml-configuration.asciidoc[]
include::ml-configuration.asciidoc[leveloffset=+1]

include::create-jobs.asciidoc[leveloffset=+1]
include::create-jobs.asciidoc[leveloffset=+2]

include::job-tips.asciidoc[leveloffset=+2]
include::job-tips.asciidoc[leveloffset=+3]

include::stopping-ml.asciidoc[leveloffset=+1]
include::stopping-ml.asciidoc[leveloffset=+2]

include::ml-api-quickref.asciidoc[]
include::ml-api-quickref.asciidoc[leveloffset=+1]

include::ootb-ml-jobs.asciidoc[]
include::ootb-ml-jobs.asciidoc[leveloffset=+1]

include::ootb-ml-jobs-apache.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-apache.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-apm.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-apm.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-auditbeat.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-auditbeat.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-logs-ui.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-logs-ui.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-metricbeat.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-metricbeat.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-nginx.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-nginx.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-siem.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-siem.asciidoc[leveloffset=+2]

include::ootb-ml-jobs-uptime.asciidoc[leveloffset=+1]
include::ootb-ml-jobs-uptime.asciidoc[leveloffset=+2]
////
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-functions.asciidoc[leveloffset=+1]

include::{es-repo-dir}/ml/anomaly-detection/functions.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-count-functions.asciidoc[leveloffset=+2]

include::anomaly-examples.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-geo-functions.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/customurl.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-info-functions.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/aggregations.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-metric-functions.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/detector-custom-rules.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-rare-functions.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/categories.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-sum-functions.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/populations.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/functions/ml-time-functions.asciidoc[leveloffset=+2]
////
include::anomaly-examples.asciidoc[leveloffset=+1]
////
include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-url.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/transforms.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-aggregations.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/delayed-data-detection.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc[leveloffset=+2]

include::ml-limitations.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-categories.asciidoc[leveloffset=+2]

//include::ml-troubleshooting.asciidoc[]
include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-populations.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-transform.asciidoc[leveloffset=+2]

include::{es-repo-dir}/ml/anomaly-detection/ml-delayed-data-detection.asciidoc[leveloffset=+2]
////
include::ml-limitations.asciidoc[leveloffset=+1]

//include::ml-troubleshooting.asciidoc[leveloffset=+1]
16 changes: 8 additions & 8 deletions docs/en/stack/ml/anomaly-detection/job-tips.asciidoc
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[role="xpack"]
[[job-tips]]
== Machine learning job tips
= Machine learning job tips
++++
<titleabbrev>Job tips</titleabbrev>
++++
Expand All @@ -12,7 +12,7 @@ results.

[discrete]
[[bucket-span]]
=== 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 an {anomaly-job} in {kib}, you can
Expand All @@ -27,7 +27,7 @@ information about choosing an appropriate bucket span, see <<ml-buckets>>.

[discrete]
[[cardinality]]
=== Cardinality
== Cardinality

If there are logical groupings of related entities in your data, {ml} analytics
can make data models and generate results that take these groupings into
Expand All @@ -41,11 +41,11 @@ 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 <<ml-configuring-pop>>.
field to use. For more information, see <<ml-configuring-populations>>.

[discrete]
[[detectors]]
=== Detectors
== Detectors

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
Expand All @@ -58,14 +58,14 @@ duplicates if they have the same `function`, `field_name`, `by_field_name`,

[discrete]
[[influencers]]
=== Influencers
== Influencers

See <<ml-influencers>>.


[discrete]
[[model-memory-limits]]
=== Model memory limits
== Model memory limits

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
Expand Down Expand Up @@ -108,7 +108,7 @@ increase the size of the {ml} nodes in your cluster.

[discrete]
[[dedicated-indices]]
=== Dedicated indices
== Dedicated indices

For each {anomaly-job}, you can optionally specify a dedicated index to store
the {anomaly-detect} results. As {anomaly-jobs} may produce a large amount
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-api-quickref.asciidoc
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[role="xpack"]
[[ml-api-quickref]]
== API quick reference
= API quick reference

All {ml} {anomaly-detect} endpoints have the following base:

Expand Down
4 changes: 2 additions & 2 deletions docs/en/stack/ml/anomaly-detection/ml-buckets.asciidoc
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[role="xpack"]
[[ml-buckets]]
== Buckets
= Buckets

The {ml-features} use the concept of a _bucket_ to divide the time series into
batches for processing.
Expand All @@ -20,7 +20,7 @@ The bucket span has a significant impact on the analysis. When you’re trying t

[discrete]
[[ml-bucket-results]]
=== Bucket results
== Bucket results

When you view your {ml} results, each bucket has an anomaly score. This score is
a statistically aggregated and normalized view of the combined anomalousness of
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-calendars.asciidoc
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[role="xpack"]
[[ml-calendars]]
== Calendars and scheduled events
= Calendars and scheduled events

Sometimes there are periods when you expect unusual activity to take place,
such as bank holidays, "Black Friday", or planned system outages. If you
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-concepts.asciidoc
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[role="xpack"]
[[ml-concepts]]
== Concepts
= Concepts

This section explains the fundamental concepts of the Elastic {ml}
{anomaly-detect} feature.
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-configuration.asciidoc
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[role="xpack"]
[[ml-configuration]]
== Configure {anomaly-detect}
= Configure {anomaly-detect}

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,
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-datafeeds.asciidoc
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[role="xpack"]
[[ml-datafeeds]]
== {dfeeds-cap}
= {dfeeds-cap}

{anomaly-jobs-cap} can analyze data that is stored in {es} or data that is
sent from some other source via an API. _{dfeeds-cap}_ retrieve data from {es}
Expand Down
4 changes: 2 additions & 2 deletions docs/en/stack/ml/anomaly-detection/ml-influencers.asciidoc
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[role="xpack"]
[[ml-influencers]]
== Influencers
= Influencers

When anomalous events occur, we want to know why. To determine the cause,
however, you often need a broader knowledge of the domain. If you have
Expand Down Expand Up @@ -40,7 +40,7 @@ can be overwhelming and there is a small overhead to the analysis.

[discrete]
[[ml-influencer-results]]
=== Influencer results
== Influencer results

The influencer results show which entities were anomalous and when. One
influencer result is written per bucket for each influencer that affects the
Expand Down
2 changes: 1 addition & 1 deletion docs/en/stack/ml/anomaly-detection/ml-jobs.asciidoc
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[role="xpack"]
[[ml-jobs]]
== {anomaly-jobs-cap}
= {anomaly-jobs-cap}
++++
<titleabbrev>Jobs</titleabbrev>
++++
Expand Down
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