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[DOCS] Reworks the Examples section in anomaly detection docs (elastic#2181)
* [DOCS] Reworks the Examples section in anomaly detection docs. * [DOCS] Changes attribute. * [DOCS] Adds getting started link to the overview section. * Renames page to How-to guides. * Adjusts getting started subpages. * Renames example file and adds redirect. * Changes reference name on overview page. * Removes references from Advanced concepts page. Co-authored-by: Elastic Machine <[email protected]>
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docs/en/stack/ml/anomaly-detection/anomaly-examples.asciidoc renamed to docs/en/stack/ml/anomaly-detection/anomaly-how-tos.asciidoc

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[[anomaly-examples]]
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= {anomaly-detect-cap} examples
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[[anomaly-how-tos]]
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= How-to guides
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<titleabbrev>Examples</titleabbrev>
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<titleabbrev>How-tos</titleabbrev>
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Though it is quite simple to analyze your data and provide quick {ml} results,
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gaining deep insights might require some additional planning and configuration.
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The scenarios in this section describe some best practices for generating useful
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The guides in this section describe some best practices for generating useful
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{ml} results and insights from your data.
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* <<ml-getting-started>>
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* <<ml-configuring-alerts>>
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* <<ml-configuring-aggregation>>
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* <<ml-configuring-aggregation, Using aggregations in {dfeeds}>>
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* <<ml-configuring-transform, Using runtime fields in {dfeeds}>>
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* <<ml-configuring-detector-custom-rules>>
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* <<ml-configuring-categories>>
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* <<ml-reverting-model-snapshot>>
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* <<geographic-anomalies>>
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* <<mapping-anomalies>>
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* <<ml-configuring-populations>>
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* <<ml-configuring-transform>>
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* <<ml-configuring-url>>
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* <<ml-delayed-data-detection>>
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* <<mapping-anomalies>>
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* <<ml-jobs-from-lens>>
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* <<move-jobs>>
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docs/en/stack/ml/anomaly-detection/index.asciidoc

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include::ml-ad-forecast.asciidoc[leveloffset=+2]
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include::../get-started/index.asciidoc[leveloffset=+1]
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include::ml-ad-concepts.asciidoc[leveloffset=+1]
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include::ml-ad-algorithms.asciidoc[leveloffset=+2]
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include::anomaly-detection-scale.asciidoc[leveloffset=+2]
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include::ml-api-quickref.asciidoc[leveloffset=+1]
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include::{es-repo-dir}/ml/anomaly-detection/ml-delayed-data-detection.asciidoc[leveloffset=+2]
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include::anomaly-examples.asciidoc[leveloffset=+1]
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include::ml-api-quickref.asciidoc[leveloffset=+1]
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include::../get-started/index.asciidoc[leveloffset=+2]
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include::anomaly-how-tos.asciidoc[leveloffset=+1]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-alerts.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-aggregations.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-transform.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-detector-custom-rules.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-categories.asciidoc[leveloffset=+2]
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include::geographic-anomalies.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-populations.asciidoc[leveloffset=+2]
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include::mapping-anomalies.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-transform.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-populations.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-configuring-url.asciidoc[leveloffset=+2]
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include::{es-repo-dir}/ml/anomaly-detection/ml-delayed-data-detection.asciidoc[leveloffset=+2]
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include::mapping-anomalies.asciidoc[leveloffset=+2]
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include::ml-jobs-from-visuals.asciidoc[leveloffset=+2]
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include::../move-jobs.asciidoc[leveloffset=+2]

docs/en/stack/ml/anomaly-detection/ml-ad-concepts.asciidoc

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* <<ml-ad-algorithms>>
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* <<anomaly-detection-scale>>
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* <<ml-delayed-data-detection, Delayed data>>

docs/en/stack/ml/anomaly-detection/ml-ad-overview.asciidoc

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You can use {stack} {ml-features} to analyze time series data and identify anomalous patterns in your data set.
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* <<ml-ad-finding-anomalies>>
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* <<ml-getting-started>>
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* <<ml-ad-concepts>>
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* <<ml-api-quickref>>
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* <<anomaly-examples>>
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* <<anomaly-how-tos>>
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* <<ml-ad-resources>>

docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc

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[role="xpack"]
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[discrete]
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[[sample-data-forecasts]]
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= Create forecasts
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docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc

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[role="xpack"]
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[discrete]
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[[sample-data-jobs]]
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= Create sample {anomaly-jobs} in {kib}
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<titleabbrev>Create {anomaly-jobs}</titleabbrev>
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The {kib} sample data sets include some pre-configured {anomaly-jobs} for you to
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play with. You can use either of the following methods to add the jobs:

docs/en/stack/ml/get-started/ml-gs-next.asciidoc

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[role="xpack"]
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[discrete]
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[[sample-data-next]]
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= Next steps
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docs/en/stack/ml/get-started/ml-gs-results.asciidoc

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[role="xpack"]
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[discrete]
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[[sample-data-results]]
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= View {anomaly-detect} results
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docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc

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[role="xpack"]
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[discrete]
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[[sample-data-visualizer]]
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= Explore the data in {kib}
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docs/en/stack/ml/redirects.asciidoc

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[role="exclude",id="security-winlogbeat-authentication-jobs"]
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=== Security: {winlogbeat} authentication
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These {anomaly-jobs} were removed in 8.3.0.
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These {anomaly-jobs} were removed in 8.3.0.
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[role="exclude",id="anomaly-examples"]
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=== Examples
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This page has moved. See <<anomaly-how-tos>>.

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