Merge remote-tracking branch 'eedugon/cloud_account_refinement' into cloud_account_refinement

Author: eedugon

.github/workflows/docs-build.yml

@@ -7,7 +7,7 @@ jobs:
   preview:
     uses: elastic/docs-builder/.github/workflows/preview-build.yml@main
     with:
-      continue-on-error: true
+      continue-on-error: false
       strict: false
     permissions:
       contents: read

.gitignore

@@ -1,2 +1,3 @@
 # docs-builder default output
-.artifacts
+.artifacts
+.DS_store

deploy-manage/deploy/elastic-cloud.md

@@ -14,6 +14,7 @@ Serverless projects use the core components of the {{stack}}, such as {{es}} and
 Elastic provides three serverless solutions available on {{ecloud}}:
 
 * **https://www.elastic.co/guide/en/serverless/current/what-is-elasticsearch-serverless.html[{{es-serverless}}]**: Build powerful applications and search experiences using a rich ecosystem of vector search capabilities, APIs, and libraries.
+% See solutions/search/serverless-elasticsearch-get-started.md
 * **https://www.elastic.co/guide/en/serverless/current/what-is-observability-serverless.html[{{obs-serverless}}]**: Monitor your own platforms and services using powerful machine learning and analytics tools with your logs, metrics, traces, and APM data.
 * **https://www.elastic.co/guide/en/serverless/current/what-is-security-serverless.html[{{sec-serverless}}]**: Detect, investigate, and respond to threats with SIEM, endpoint protection, and AI-powered analytics capabilities.
 

explore-analyze/alerts/kibana/rule-type-es-query.md

@@ -37,7 +37,7 @@ When you create an {{es}} query rule, your choice of query type affects the info
 
     If you use [KQL](../../query-filter/languages/kql.md) or [Lucene](../../query-filter/languages/lucene-query-syntax.md), you must specify a data view then define a text-based query. For example, `http.request.referrer: "https://example.com"`.
 
-    If you use [ES|QL](../../query-filter/languages/esorql.md), you must provide a source command followed by an optional series of processing commands, separated by pipe characters (|). [8.16.0] For example:
+    If you use [ES|QL](../../query-filter/languages/esql.md), you must provide a source command followed by an optional series of processing commands, separated by pipe characters (|). [8.16.0] For example:
 
     ```sh
     FROM kibana_sample_data_logs

explore-analyze/discover/try-esql.md

@@ -10,7 +10,7 @@ The Elasticsearch Query Language, {{esql}}, makes it easier to explore your data
 In this tutorial we’ll use the {{kib}} sample web logs in Discover and Lens to explore the data and create visualizations.
 
 ::::{tip}
-For the complete {{esql}} documentation, including tutorials, examples and the full syntax reference, refer to the [{{es}} documentation](../query-filter/languages/esorql.md). For a more detailed overview of {{esql}} in {{kib}}, refer to [Use {{esql}} in Kibana](../query-filter/languages/esql-kibana.md).
+For the complete {{esql}} documentation, including tutorials, examples and the full syntax reference, refer to the [{{es}} documentation](../query-filter/languages/esql.md). For a more detailed overview of {{esql}} in {{kib}}, refer to [Use {{esql}} in Kibana](../query-filter/languages/esql-kibana.md).
 
 ::::
 

explore-analyze/geospatial-analysis.md

@@ -34,7 +34,7 @@ Data is often messy and incomplete. [Ingest pipelines](../manage-data/ingest/tra
 
 ## ES|QL [esql-query]
 
-[ES|QL](query-filter/languages/esorql.md) has support for [Geospatial Search](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-spatial-functions) functions, enabling efficient index searching for documents that intersect with, are within, are contained by, or are disjoint from a query geometry. In addition, the `ST_DISTANCE` function calculates the distance between two points.
+[ES|QL](query-filter/languages/esql.md) has support for [Geospatial Search](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-spatial-functions) functions, enabling efficient index searching for documents that intersect with, are within, are contained by, or are disjoint from a query geometry. In addition, the `ST_DISTANCE` function calculates the distance between two points.
 
 * [`ST_INTERSECTS`](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-st_intersects)
 * [`ST_DISJOINT`](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-st_disjoint)

explore-analyze/machine-learning.md

@@ -8,27 +8,21 @@ mapped_urls:
 
 # What is Elastic Machine Learning? [machine-learning-intro]
 
-{{ml-cap}} features analyze your data and generate models for its patterns of behavior.
-The type of analysis that you choose depends on the questions or problems you want to address and the type of data you have available.
+{{ml-cap}} features analyze your data and generate models for its patterns of behavior. The type of analysis that you choose depends on the questions or problems you want to address and the type of data you have available.
 
 ## Unsupervised {{ml}} [machine-learning-unsupervised]
 
 There are two types of analysis that can deduce the patterns and relationships within your data without training or intervention: *{{anomaly-detect}}* and *{{oldetection}}*.
 
-[{{anomaly-detect-cap}}](machine-learning/anomaly-detection.md) requires time series data.
-It constructs a probability model and can run continuously to identify unusual events as they occur. The model evolves over time; you can use its insights to forecast future behavior.
+[{{anomaly-detect-cap}}](machine-learning/anomaly-detection.md) requires time series data. It constructs a probability model and can run continuously to identify unusual events as they occur. The model evolves over time; you can use its insights to forecast future behavior.
 
-[{{oldetection-cap}}](machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md) does not require time series data.
-It is a type of {{dfanalytics}} that identifies unusual points in a data set by analyzing how close each data point is to others and the density of the cluster of points around it.
-It does not run continuously; it generates a copy of your data set where each data point is annotated with an {{olscore}}.
-The score indicates the extent to which a data point is an outlier compared to other data points.
+[{{oldetection-cap}}](machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md) does not require time series data. It is a type of {{dfanalytics}} that identifies unusual points in a data set by analyzing how close each data point is to others and the density of the cluster of points around it. It does not run continuously; it generates a copy of your data set where each data point is annotated with an {{olscore}}. The score indicates the extent to which a data point is an outlier compared to other data points.
 
 ## Supervised {{ml}} [machine-learning-supervised]
 
 There are two types of {{dfanalytics}} that require training data sets: *{{classification}}* and *{{regression}}*.
 
-In both cases, the result is a copy of your data set where each data point is annotated with predictions and a trained model, which you can deploy to make predictions for new data.
-For more information, refer to [Introduction to supervised learning](machine-learning/data-frame-analytics/ml-dfa-overview.md#ml-supervised-workflow).
+In both cases, the result is a copy of your data set where each data point is annotated with predictions and a trained model, which you can deploy to make predictions for new data. For more information, refer to [Introduction to supervised learning](machine-learning/data-frame-analytics/ml-dfa-overview.md#ml-supervised-workflow).
 
 [{{classification-cap}}](machine-learning/data-frame-analytics/ml-dfa-classification.md) learns relationships between your data points in order to predict discrete categorical values, such as whether a DNS request originates from a malicious or benign domain.
 
@@ -44,18 +38,13 @@ The {{ml-features}} that are available vary by project type:
 
 ## Synchronize saved objects [machine-learning-synchronize-saved-objects]
 
-Before you can view your {{ml}} {dfeeds}, jobs, and trained models in {{kib}}, they must have saved objects.
-For example, if you used APIs to create your jobs, wait for automatic synchronization or go to the **{{ml-app}}** page and click **Synchronize saved objects**.
+Before you can view your {{ml}} {dfeeds}, jobs, and trained models in {{kib}}, they must have saved objects. For example, if you used APIs to create your jobs, wait for automatic synchronization or go to the **{{ml-app}}** page and click **Synchronize saved objects**.
 
 ## Export and import jobs [machine-learning-export-and-import-jobs]
 
-You can export and import your {{ml}} job and {{dfeed}} configuration details on the **{{ml-app}}** page.
-For example, you can export jobs from your test environment and import them in your production environment.
+You can export and import your {{ml}} job and {{dfeed}} configuration details on the **{{ml-app}}** page. For example, you can export jobs from your test environment and import them in your production environment.
 
-The exported file contains configuration details; it does not contain the {{ml}} models.
-For {{anomaly-detect}}, you must import and run the job to build a model that is accurate for the new environment.
-For {{dfanalytics}}, trained models are portable; you can import the job then transfer the model to the new cluster.
-Refer to [Exporting and importing {{dfanalytics}} trained models](machine-learning/data-frame-analytics/ml-trained-models.md#export-import).
+The exported file contains configuration details; it does not contain the {{ml}} models. For {{anomaly-detect}}, you must import and run the job to build a model that is accurate for the new environment. For {{dfanalytics}}, trained models are portable; you can import the job then transfer the model to the new cluster. Refer to [Exporting and importing {{dfanalytics}} trained models](machine-learning/data-frame-analytics/ml-trained-models.md#export-import).
 
 There are some additional actions that you must take before you can successfully import and run your jobs:
 

explore-analyze/machine-learning/anomaly-detection.md

@@ -4,13 +4,13 @@ mapped_urls:
   - https://www.elastic.co/guide/en/kibana/current/xpack-ml-anomalies.html
 ---
 
-# Anomaly detection
+# Anomaly detection [ml-ad-overview]
 
-% What needs to be done: Align serverless/stateful
+You can use {{stack}} {{ml-features}} to analyze time series data and identify anomalous patterns in your data set.
 
-% Scope notes: Colleen McGinnis removed "https://www.elastic.co/guide/en/serverless/current/observability-machine-learning.html" and "All children" because this page is also used below in "AIOps Labs" with "All children" selected. We can't copy all children to two places.
-
-% Use migrated content from existing pages that map to this page:
-
-% - [ ] ./raw-migrated-files/stack-docs/machine-learning/ml-ad-overview.md
-% - [ ] ./raw-migrated-files/kibana/kibana/xpack-ml-anomalies.md
+* [Finding anomalies](../../explore-analyze/machine-learning/anomaly-detection/ml-ad-finding-anomalies.md)
+* [Tutorial: Getting started with {{anomaly-detect}}](../../explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md)
+* [*Advanced concepts*](../../explore-analyze/machine-learning/anomaly-detection/ml-ad-concepts.md)
+* [*API quick reference*](../../explore-analyze/machine-learning/anomaly-detection/ml-api-quickref.md)
+* [How-tos](../../explore-analyze/machine-learning/anomaly-detection/anomaly-how-tos.md)
+* [*Resources*](../../explore-analyze/machine-learning/anomaly-detection/ml-ad-resources.md)