[mdx → asciidoc] Add AsciiDoc serverless files

.github/pull_request_template.md

@@ -19,14 +19,13 @@ Closes # <!-- Add the issue this PR closes here -->
 Add labels to:
 1. Backport to other versions (`backport-*`):
     - `backport-8.x` to backport to the latest minor
-    - `backport-skip` to not backport (for example, for serverless docs) 
+    - `backport-skip` to not backport (for example, for serverless docs)
     - `backport-main` to "backport" to `main` if the target branch is _not_ `main`
     - Individual `backport-*` labels to target specific minor versions
 2. Surface blocking reviews (`needs-*-review`):
     - `needs-writer-review` for codeowners
     - `needs-dev-review` for dev team
     - `needs-product-review` for PM review
-3. Build serverless preview docs: `ci:doc-build`
 -->
 
 - [ ] Product/Engineering Review

README.md

@@ -10,7 +10,7 @@ Within this repo, the `/docs/en/` directory is structured as follows:
 | --------------------- | ----------- |
 | __integrations__      | Contains the source files for the [Integrations Developer Guide](https://www.elastic.co/guide/en/integrations-developer/current/index.html).
 | __observability__     | Contains the source files for the [Observability Guide](https://www.elastic.co/guide/en/observability/current/index.html), which includes content for APM, Logs, Metrics, Synthetics, User experience, and Uptime.|
-| __serverless__ | Contains the source files for the [Elastic Observability Serverless docs](https://docs.elastic.co/serverless/observability/what-is-observability-serverless).
+| __serverless__ | Contains the source files for the [Elastic Observability Serverless docs](https://www.elastic.co/docs/current/serverless/observability/what-is-observability-serverless).
 | __shared__ | Contains the source files for shared Observability content.|
 | __templates__ | Contains content templates.|
 
@@ -38,7 +38,19 @@ If you prefer to use aliases, you can load the [elastic/docs/doc_build_aliases.s
 
 ### Elastic Observability Serverless docs
 
-The Elastic Observability Serverless docs use a custom syntax written in [MDX](https://mdxjs.com/). In many cases, you only need to know plain Markdown to contribute. We'll add a public component reference and additional contribution guidelines in future. Elasticians can refer to our [internal syntax reference](https://docs.elastic.dev/docsmobile/syntax).
+The source files for the Serverless docs are written in [AsciiDoc](https://docs.asciidoctor.org/asciidoc/latest/) and are built using [elastic/docs](https://github.com/elastic/docs).
+
+To build the docs locally:
+
+1. Check out the `elastic/docs` repository, along with any repositories that contain source files.
+2. Run the `build_docs` script, passing in the path to the `index.asciidoc` and resource paths to other repos that contain source files. For example, to build the Observability Guide and open it in the browser, run:
+    ```
+    ../docs/build_docs --doc ../docs-content/serverless/index.asciidoc --chunk 5 --open --resource ./docs/en/serverless --resource ../security-docs/docs/serverless --resource ../docs-content/serverless
+    ```
+
+The above command assumes that this repo, [elastic/docs](https://github.com/elastic/docs), [elastic/observability-docs](https://github.com/elastic/observability-docs), [elastic/security-docs](https://github.com/elastic/security-docs), and [elastic/docs-content](https://github.com/elastic/docs-content) are checked out into the same parent directory.
+
+If you prefer to use aliases, you can load the [elastic/docs/doc_build_aliases.sh file](https://github.com/elastic/docs/blob/master/doc_build_aliases.sh), which has the resources defined for you.
 
 ### Integrations Developer Guide
 

docs/en/serverless/aiops/aiops-analyze-spikes.asciidoc

@@ -0,0 +1,73 @@
+[[observability-aiops-analyze-spikes]]
+= Analyze log spikes and drops
+
+// :description: Find and investigate the causes of unusual spikes or drops in log rates.
+// :keywords: serverless, observability, how-to
+
+preview:[]
+
+// <DocCallOut template="technical preview" />
+
+{observability} provides built-in log rate analysis capabilities,
+based on advanced statistical methods,
+to help you find and investigate the causes of unusual spikes or drops in log rates.
+
+To analyze log spikes and drops:
+
+. In your {observability} project, go to **AIOps** → **Log rate analysis**.
+. Choose a data view or saved search to access the log data you want to analyze.
+. In the histogram chart, click a spike (or drop) to start the analysis.
++
+[role="screenshot"]
+image::images/log-rate-histogram.png[Histogram showing log spikes and drops ]
++
+When the analysis runs, it identifies statistically significant field-value combinations that contribute to the spike or drop,
+and then displays them in a table:
++
+[role="screenshot"]
+image::images/log-rate-analysis-results.png[Histogram showing log spikes and drops ]
++
+Notice that you can optionally turn on **Smart grouping** to summarize the results into groups.
+You can also click **Filter fields** to remove fields that are not relevant.
++
+The table shows an indicator of the level of impact and a sparkline showing the shape of the impact in the chart.
+. Select a row to display the impact of the field on the histogram chart.
+. From the **Actions** menu in the table, you can choose to view the field in **Discover**,
+view it in <<log-pattern-analysis,Log Pattern Analysis>>,
+or copy the table row information to the clipboard as a query filter.
+
+To pin a table row, click the row, then move the cursor to the histogram chart.
+It displays a tooltip with exact count values for the pinned field which enables closer investigation.
+
+Brushes in the chart show the baseline time range and the deviation in the analyzed data.
+You can move the brushes to redefine both the baseline and the deviation and rerun the analysis with the modified values.
+
+[discrete]
+[[log-pattern-analysis]]
+== Log pattern analysis
+
+// <DocCallOut template="technical preview" />
+
+Use log pattern analysis to find patterns in unstructured log messages and examine your data.
+When you run a log pattern analysis, it performs categorization analysis on a selected field,
+creates categories based on the data, and then displays them together in a chart.
+The chart shows the distribution of each category and an example document that matches the category.
+Log pattern analysis is useful when you want to examine how often different types of logs appear in your data set.
+It also helps you group logs in ways that go beyond what you can achieve with a terms aggregation.
+
+To run log pattern analysis:
+
+. Follow the steps under <<observability-aiops-analyze-spikes>> to run a log rate analysis.
+. From the **Actions** menu, choose **View in Log Pattern Analysis**.
+. Select a category field and optionally apply any filters that you want.
+. Click **Run pattern analysis**.
++
+The results of the analysis are shown in a table:
++
+[role="screenshot"]
+image::images/log-pattern-analysis.png[Log pattern analysis of the message field ]
+. From the **Actions** menu, click the plus (or minus) icon to open **Discover** and show (or filter out) the given category there, which helps you to further examine your log messages.
+
+// TODO: Question: Is the log pattern analysis only available through the log rate analysis UI?
+
+// TODO: Add some good examples to this topic taken from existing docs or recommendations from reviewers.