Change aiops to ml

docs/en/serverless/alerting/create-manage-rules.asciidoc

@@ -24,7 +24,7 @@ Learn more about Observability rules and how to create them:
 |===
 | Rule type | Name | Detects when...
 
-| AIOps
+| Machine learning
 | <<observability-create-anomaly-alert-rule,Anomaly detection>>
 | Anomalies match specific conditions.
 

docs/en/serverless/alerting/aiops-generate-anomaly-alerts.asciidoc → docs/en/serverless/alerting/generate-anomaly-alerts.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-generate-anomaly-alerts]]
+[[observability-generate-anomaly-alerts]]
 = Create an anomaly detection rule
 
 // :description: Get alerts when anomalies match specific conditions.
@@ -29,7 +29,7 @@ To create an anomaly detection rule:
 
 . In your {obs-serverless} project, go to **AIOps** → **Anomaly detection**.
 . In the list of anomaly detection jobs, find the job you want to check for anomalies.
-Haven't created a job yet? <<observability-aiops-detect-anomalies,Create one now>>.
+Haven't created a job yet? <<observability-detect-anomalies,Create one now>>.
 . From the **Actions** menu next to the job, select **Create alert rule**.
 . Specify a name and optional tags for the rule. You can use these tags later to filter alerts.
 . Verify that the correct job is selected and configure the alert details:
@@ -80,7 +80,7 @@ Alerts generated by these rules do not appear on the **Alerts** page.
 ====
 
 [discrete]
-[[observability-aiops-generate-anomaly-alerts-add-actions]]
+[[observability-generate-anomaly-alerts-add-actions]]
 == Add actions
 
 You can extend your rules with actions that interact with third-party systems, write to logs or indices, or send user notifications. You can add an action to a rule at any time. You can create rules without adding actions, and you can also define multiple actions for a single rule.
@@ -189,7 +189,7 @@ The typical value for the bucket, according to analytical modeling.
 =====
 
 [discrete]
-[[observability-aiops-generate-anomaly-alerts-edit-an-anomaly-detection-rule]]
+[[observability-generate-anomaly-alerts-edit-an-anomaly-detection-rule]]
 == Edit an anomaly detection rule
 
 To edit an anomaly detection rule:

docs/en/serverless/index.asciidoc

@@ -160,7 +160,7 @@ include::./incident-management.asciidoc[leveloffset=+2]
 // Alerting
 include::./alerting/alerting.asciidoc[leveloffset=+3]
 include::./alerting/create-manage-rules.asciidoc[leveloffset=+4]
-include::./alerting/aiops-generate-anomaly-alerts.asciidoc[leveloffset=+5]
+include::./alerting/generate-anomaly-alerts.asciidoc[leveloffset=+5]
 include::./alerting/create-anomaly-alert-rule.asciidoc[leveloffset=+5]
 include::./alerting/create-custom-threshold-alert-rule.asciidoc[leveloffset=+5]
 include::./alerting/create-elasticsearch-query-alert-rule.asciidoc[leveloffset=+5]
@@ -194,14 +194,14 @@ include::./monitor-datasets.asciidoc[leveloffset=+2]
 //Observability AI Assistant
 include::./ai-assistant/ai-assistant.asciidoc[leveloffset=+2]
 
-//AIOPS
+//Machine learning
 
-include::./aiops/aiops.asciidoc[leveloffset=+2]
-include::./aiops/aiops-detect-anomalies.asciidoc[leveloffset=+3]
-include::./aiops/aiops-tune-anomaly-detection-job.asciidoc[leveloffset=+4]
-include::./aiops/aiops-forecast-anomaly.asciidoc[leveloffset=+4]
-include::./aiops/aiops-analyze-spikes.asciidoc[leveloffset=+3]
-include::./aiops/aiops-detect-change-points.asciidoc[leveloffset=+3]
+include::./machine-learning/machine-learning.asciidoc[leveloffset=+2]
+include::./machine-learning/machine-learning-detect-anomalies.asciidoc[leveloffset=+3]
+include::./machine-learning/machine-learning-tune-anomaly-detection-job.asciidoc[leveloffset=+4]
+include::./machine-learning/machine-learning-forecast-anomaly.asciidoc[leveloffset=+4]
+include::./machine-learning/machine-learning-analyze-spikes.asciidoc[leveloffset=+3]
+include::./machine-learning/machine-learning-detect-change-points.asciidoc[leveloffset=+3]
 
 // Reference group
 

docs/en/serverless/logging/log-monitoring.asciidoc

@@ -96,7 +96,7 @@ Use **Logs Explorer** to search, filter, and tail all your logs ingested into yo
 The following resources provide information on viewing and monitoring your logs:
 
 * <<observability-discover-and-explore-logs,Discover and explore>>: Discover and explore all of the log events flowing in from your servers, virtual machines, and containers in a centralized view.
-* <<observability-aiops-detect-anomalies,Detect log anomalies>>: Use {ml} to detect log anomalies automatically.
+* <<observability-detect-anomalies,Detect log anomalies>>: Use {ml} to detect log anomalies automatically.
 
 [discrete]
 [[observability-log-monitoring-monitor-data-sets]]

docs/en/serverless/aiops/aiops-analyze-spikes.asciidoc → docs/en/serverless/machine-learning/analyze-spikes.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-analyze-spikes]]
+[[observability-analyze-spikes]]
 = Analyze log spikes and drops
 
 // :description: Find and investigate the causes of unusual spikes or drops in log rates.
@@ -57,7 +57,7 @@ It also helps you group logs in ways that go beyond what you can achieve with a
 
 To run log pattern analysis:
 
-. Follow the steps under <<observability-aiops-analyze-spikes>> to run a log rate analysis.
+. Follow the steps under <<observability-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**.

docs/en/serverless/aiops/aiops-detect-anomalies.asciidoc → docs/en/serverless/machine-learning/detect-anomalies.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-detect-anomalies]]
+[[observability-detect-anomalies]]
 = Detect anomalies
 
 // :description: Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
@@ -102,15 +102,15 @@ Expand the fields to see details about the range and distribution of values.
 When you're done, go back to the first step and create your job.
 ====
 . Step through the instructions in the job creation wizard to configure your job.
-You can accept the default settings for most settings now and <<observability-aiops-tune-anomaly-detection-job,tune the job>> later.
+You can accept the default settings for most settings now and <<observability-tune-anomaly-detection-job,tune the job>> later.
 . If you want the job to start immediately when the job is created, make sure that option is selected on the summary page.
 . When you're done, click **Create job**.
 When the job runs, the {ml} features analyze the input stream of data, model its behavior, and perform analysis based on the detectors in each job.
 When an event occurs outside of the baselines of normal behavior, that event is identified as an anomaly.
 . After the job is started, click **View results**.
 
 [discrete]
-[[observability-aiops-detect-anomalies-view-the-results]]
+[[observability-detect-anomalies-view-the-results]]
 == View the results
 
 After the anomaly detection job has processed some data,
@@ -188,7 +188,7 @@ By default, the **Anomalies** table contains all anomalies that have a severity
 If you are only interested in critical anomalies, for example, you can change the severity threshold for this table.
 . (Optional) From the **Actions** menu in the **Anomalies** table, you can choose to view relevant documents in **Discover** or create a job rule.
 Job rules instruct anomaly detectors to change their behavior based on domain-specific knowledge that you provide.
-To learn more, refer to <<observability-aiops-tune-anomaly-detection-job>>
+To learn more, refer to <<observability-tune-anomaly-detection-job>>
 
 After you have identified anomalies, often the next step is to try to determine
 the context of those situations. For example, are there other factors that are
@@ -265,11 +265,11 @@ The list of anomalies uses the record-level anomaly scores.
 ====
 
 [discrete]
-[[observability-aiops-detect-anomalies-next-steps]]
+[[observability-detect-anomalies-next-steps]]
 == Next steps
 
 After setting up an anomaly detection job, you may want to:
 
-* <<observability-aiops-tune-anomaly-detection-job>>
-* <<observability-aiops-forecast-anomalies>>
-* <<observability-aiops-generate-anomaly-alerts>>
+* <<observability-tune-anomaly-detection-job>>
+* <<observability-forecast-anomalies>>
+* <<observability-generate-anomaly-alerts>>

docs/en/serverless/aiops/aiops-detect-change-points.asciidoc → docs/en/serverless/machine-learning/detect-change-points.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-detect-change-points]]
+[[observability-detect-change-points]]
 = Detect change points
 
 // :description: Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data.

docs/en/serverless/aiops/aiops-forecast-anomaly.asciidoc → docs/en/serverless/machine-learning/forecast-anomaly.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-forecast-anomalies]]
+[[observability-forecast-anomalies]]
 = Forecast future behavior
 
 // :description: Predict future behavior of your data by creating a forecast for an anomaly detection job.
@@ -25,7 +25,7 @@ For example, you might want to determine how likely it is that your disk utiliza
 
 To create a forecast:
 
-. <<observability-aiops-detect-anomalies,Create an anomaly detection job>> and view the results in the **Single Metric Viewer**.
+. <<observability-detect-anomalies,Create an anomaly detection job>> and view the results in the **Single Metric Viewer**.
 . Click **Forecast**.
 . Specify a duration for your forecast.
 This value indicates how far to extrapolate beyond the last record that was processed.

docs/en/serverless/machine-learning/machine-learning.asciidoc

@@ -0,0 +1,24 @@
+[[observability-machine-learning]]
+= Machine learning
+
+// :description: Automate anomaly detection and accelerate root cause analysis with machine learning.
+// :keywords: serverless, observability, overview
+
+preview:[]
+
+The machine learning capabilities available in {obs-serverless} enable you to consume and process large observability data sets at scale, reducing the time and effort required to detect, understand, investigate, and resolve incidents.
+Built on predictive analytics and {ml}, these capabilities require no prior experience with {ml}.
+DevOps engineers, SREs, and security analysts can get started right away using these features with little or no advanced configuration:
+
+|===
+| Feature | Description
+
+| <<observability-detect-anomalies,Anomaly detection>>
+| Detect anomalies by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
+
+| <<observability-analyze-spikes,Log rate analysis>>
+| Find and investigate the causes of unusual spikes or drops in log rates.
+
+| <<observability-detect-change-points,Change point detection>>
+| Detect distribution changes, trend changes, and other statistically significant change points in a metric of your time series data.
+|===

docs/en/serverless/aiops/aiops-tune-anomaly-detection-job.asciidoc → docs/en/serverless/machine-learning/tune-anomaly-detection-job.asciidoc

@@ -1,4 +1,4 @@
-[[observability-aiops-tune-anomaly-detection-job]]
+[[observability-tune-anomaly-detection-job]]
 = Tune your anomaly detection job
 
 // :description: Tune your job by creating calendars, adding job rules, and defining custom URLs.

docs/en/serverless/observability-overview.asciidoc

@@ -135,8 +135,8 @@ image::images/cases.png[Screenshot showing list of cases]
 <<observability-cases,Learn more about cases → >>
 
 [discrete]
-[[observability-serverless-observability-overview-aiops]]
-== AIOps
+[[observability-serverless-observability-overview-machine-learning]]
+== Machine learning
 
 Reduce the time and effort required to detect, understand, investigate, and resolve incidents at scale
 by leveraging predictive analytics and machine learning:
@@ -148,4 +148,4 @@ by leveraging predictive analytics and machine learning:
 [role="screenshot"]
 image::images/log-rate-analysis.png[Log rate analysis page showing log rate spike ]
 
-<<observability-aiops,Learn more about AIOps →>>
+<<observability-machine-learning,Learn more about machine learning →>>

docs/en/serverless/quickstarts/monitor-hosts-with-elastic-agent.asciidoc

@@ -119,10 +119,10 @@ You can also:
 ** <<observability-monitor-datasets,Monitor log data set quality>> to find degraded documents.
 ** <<observability-run-log-pattern-analysis,Run a pattern analysis>> to find patterns in unstructured log messages.
 ** <<observability-alerting,Create alerts>> that notify you when an Observability data type reaches or exceeds a given value.
-* Use <<observability-aiops,AIOps features>> to apply predictive analytics and machine learning to your data:
+* Use <<observability-machine-learning,Machine learning features>> to apply predictive analytics and machine learning to your data:
 +
-** <<observability-aiops-detect-anomalies,Detect anomalies>> by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
-** <<observability-aiops-analyze-spikes,Analyze log spikes and drops>>.
-** <<observability-aiops-detect-change-points,Detect change points>> in your time series data.
+** <<observability-detect-anomalies,Detect anomalies>> by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
+** <<observability-analyze-spikes,Analyze log spikes and drops>>.
+** <<observability-detect-change-points,Detect change points>> in your time series data.
 
 Refer to <<observability-serverless-observability-overview>> for a description of other useful features.

docs/en/serverless/what-is-observability-serverless.asciidoc

@@ -26,7 +26,7 @@ While in technical preview, Elastic Observability serverless projects should not
 * <<observability-discover-and-explore-logs,*Explore log data*>>: Use Discover to explore your log data.
 * <<observability-create-manage-rules,*Trigger alerts and triage problems*>>: Create rules to detect complex conditions and trigger alerts.
 * <<observability-slos,*Track and deliver on your SLOs*>>: Measure key metrics important to the business.
-* <<observability-aiops-detect-anomalies,*Detect anomalies and spikes*>>: Find unusual behavior in time series data.
+* <<observability-detect-anomalies,*Detect anomalies and spikes*>>: Find unusual behavior in time series data.
 * <<observability-apm,*Monitor application performance*>>: Monitor your software services and applications in real time.
 * <<observability-apm-agents-opentelemetry,*Integrate with OpenTelemetry*>>: Reuse existing APM instrumentation to capture logs, traces, and metrics.
 * <<observability-analyze-hosts,*Monitor your hosts and services*>>: Get a metrics-driven view of your hosts backed by an interface called Lens.