elastic · florent-leborgne · Feb 10, 2025 · Feb 10, 2025
@@ -80,7 +80,7 @@ The following action variables are specific to the index threshold rule. You can
 
 ## Example [_example]
 
-In this example, you will use the {{kib}} [sample weblog data set](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) to set up and tune the conditions on an index threshold rule. For this example, you want to detect when any of the top four sites serve more than 420,000 bytes over a 24 hour period.
+In this example, you will use the {{kib}} [sample weblog data set](https://www.elastic.co/guide/en/kibana/current/get-started.html) to set up and tune the conditions on an index threshold rule. For this example, you want to detect when any of the top four sites serve more than 420,000 bytes over a 24 hour period.
 
 1. Go to **{{stack-manage-app}} > {{rules-ui}}** and click **Create rule**.
 2. Select the **Index threshold** rule type.

@@ -15,8 +15,8 @@ mapped_pages:
 $$$dashboard-minimum-requirements$$$
 To create or edit dashboards, you first need to:
 
-* have [data indexed into {{es}}](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started-index.html) and a [data view](../find-and-organize/data-views.md). A data view is a subset of your {{es}} data, and allows you to load just the right data when building a visualization or exploring it.
-    
+* have [data indexed into {{es}}](https://www.elastic.co/guide/en/starting-with-the-elasticsearch-platform-and-its-solutions/current/getting-started-general-purpose.html#gp-gs-add-data) and a [data view](../find-and-organize/data-views.md). A data view is a subset of your {{es}} data, and allows you to load just the right data when building a visualization or exploring it.
+
   ::::{tip}
   If you don’t have data at hand and still want to explore dashboards, you can import one of the [sample data sets](../../manage-data/ingest/sample-data.md) available.
   ::::

@@ -76,7 +76,7 @@ Create a drilldown that opens the **Detailed logs** dashboard from the **[Logs]
 
 ## Create URL drilldowns [create-url-drilldowns]
 
-URL drilldowns enable you to navigate from a dashboard to external websites. Destination URLs can be dynamic, depending on the dashboard context or user interaction with a panel. To create URL drilldowns, you add [variables](https://www.elastic.co/guide/en/kibana/current/url-drilldown.html#variables) to a URL template, which configures the behavior of the drilldown. All panels that you create with the visualization editors support dashboard drilldowns.
+URL drilldowns enable you to navigate from a dashboard to external websites. Destination URLs can be dynamic, depending on the dashboard context or user interaction with a panel. To create URL drilldowns, you add [variables](https://www.elastic.co/guide/en/kibana/current/drilldowns.html) to a URL template, which configures the behavior of the drilldown. All panels that you create with the visualization editors support dashboard drilldowns.
 
 ![Drilldown on pie chart that navigates to Github](../../images/kibana-dashboard_urlDrilldownGoToGitHub_8.3.gif "")
 

@@ -111,7 +111,7 @@ When working with large model sizes, consider how frequently you want to create
 
 Also consider how long you wish to retain snapshots using `model_snapshot_retention_days` and `daily_model_snapshot_retention_after_days`. Retaining fewer snapshots substantially reduces index storage requirements for model state, but also reduces the granularity of model snapshots from which you can revert.
 
-For more information, refer to [Model snapshots](https://www.elastic.co/guide/en/machine-learning/current/ml-model-snapshots.html).
+For more information, refer to [Model snapshots](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-model-snapshots).
 
 ## 12. Optimize your search queries [search-queries]
 

@@ -34,7 +34,7 @@ To get the best results from {{ml}} analytics, you must understand your data. Yo
 There are a few limitations to consider before you create this type of job:
 
 1. You cannot create forecasts for {{anomaly-jobs}} that contain geographic functions.
-2. You cannot add [custom rules with conditions](https://www.elastic.co/guide/en/machine-learning/current/ml-rules.html) to detectors that use geographic functions.
+2. You cannot add [custom rules with conditions](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-rules) to detectors that use geographic functions.
 
 If those limitations are acceptable, try creating an {{anomaly-job}} that uses the [`lat_long` function](https://www.elastic.co/guide/en/machine-learning/current/ml-geo-functions.html#ml-lat-long) to analyze your own data or the sample data sets.
 
@@ -201,7 +201,7 @@ You can also view the anomaly in **Maps** by clicking **View in Maps** in the ac
 
 When you try this type of {{anomaly-job}} with your own data, it might take some experimentation to find the best combination of buckets, detectors, and influencers to detect the type of behavior you’re seeking.
 
-For more information about {{anomaly-detect}} concepts, see [Concepts](https://www.elastic.co/guide/en/machine-learning/current/ml-concepts.html). For the full list of functions that you can use in {{anomaly-jobs}}, see [*Function reference*](ml-functions.md). For more {{anomaly-detect}} examples, see [Examples](https://www.elastic.co/guide/en/machine-learning/current/anomaly-examples.html).
+For more information about {{anomaly-detect}} concepts, see [Concepts](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-overview.html). For the full list of functions that you can use in {{anomaly-jobs}}, see [*Function reference*](ml-functions.md). For more {{anomaly-detect}} examples, see [Examples](https://www.elastic.co/guide/en/machine-learning/current/anomaly-how-tos.html).
 
 ## Add anomaly layers to your maps [geographic-anomalies-map-layer]
 

@@ -116,7 +116,7 @@ For each {{anomaly-job}}, you can optionally specify a dedicated index to store
 
 If you create {{anomaly-jobs}} in {{kib}}, you *must* use {{dfeeds}} to retrieve data from {{es}} for analysis. When you create an {{anomaly-job}}, you select a {{data-source}} and {{kib}} configures the {{dfeed}} for you under the covers.
 
-You can associate only one {{dfeed}} with each {{anomaly-job}}. The {{dfeed}} contains a query that runs at a defined interval (`frequency`). By default, this interval is calculated relative to the [bucket span](https://www.elastic.co/guide/en/machine-learning/current/ml-buckets.html) of the {{anomaly-job}}. If you are concerned about delayed data, you can add a delay before the query runs at each interval. See [Handling delayed data](ml-delayed-data-detection.md).
+You can associate only one {{dfeed}} with each {{anomaly-job}}. The {{dfeed}} contains a query that runs at a defined interval (`frequency`). By default, this interval is calculated relative to the [bucket span](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-create-job) of the {{anomaly-job}}. If you are concerned about delayed data, you can add a delay before the query runs at each interval. See [Handling delayed data](ml-delayed-data-detection.md).
 
 {{dfeeds-cap}} can also aggregate data before sending it to the {{anomaly-job}}. There are some limitations, however, and aggregations should generally be used only for low cardinality data. See [Aggregating data for faster performance](ml-configuring-aggregation.md).
 
@@ -157,7 +157,7 @@ If you want to add multiple scheduled events at once, you can import an iCalenda
 
 * You must identify scheduled events before your {{anomaly-job}} analyzes the data for that time period. Machine learning results are not updated retroactively.
 * If your iCalendar file contains recurring events, only the first occurrence is imported.
-* [Bucket results](https://www.elastic.co/guide/en/machine-learning/current/ml-bucket-results.html) are generated during scheduled events but they have an anomaly score of zero.
+* [Bucket results](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-view-results.html#ml-ad-bucket-results) are generated during scheduled events but they have an anomaly score of zero.
 * If you use long or frequent scheduled events, it might take longer for the {{ml}} analytics to learn to model your data and some anomalous behavior might be missed.
 
 ::::
@@ -192,7 +192,7 @@ You can see the list of model snapshots for each job with the [get model snapsho
 :::
 
 ::::{tip}
-There are situations other than system failures where you might want to [revert](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-revert-snapshot.html) to using a specific model snapshot. The {{ml-features}} react quickly to anomalous input and new behaviors in data. Highly anomalous input increases the variance in the models and {{ml}} analytics must determine whether it is a new step-change in behavior or a one-off event. In the case where you know this anomalous input is a one-off, it might be appropriate to reset the model state to a time before this event. For example, after a Black Friday sales day you might consider reverting to a saved snapshot. If you know about such events in advance, however, you can use [calendars and scheduled events](https://www.elastic.co/guide/en/machine-learning/current/ml-calendars.html) to avoid impacting your model.
+There are situations other than system failures where you might want to [revert](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-revert-snapshot.html) to using a specific model snapshot. The {{ml-features}} react quickly to anomalous input and new behaviors in data. Highly anomalous input increases the variance in the models and {{ml}} analytics must determine whether it is a new step-change in behavior or a one-off event. In the case where you know this anomalous input is a one-off, it might be appropriate to reset the model state to a time before this event. For example, after a Black Friday sales day you might consider reverting to a saved snapshot. If you know about such events in advance, however, you can use [calendars and scheduled events](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-calendars) to avoid impacting your model.
 ::::
 
 ## Close the job [ml-ad-close-job]

@@ -97,7 +97,7 @@ The job uses *buckets* to divide the time series into batches for processing. Fo
 
 Each {{anomaly-job}} contains one or more *detectors*, which define the type of analysis that occurs (for example, `max`, `average`, or `rare` analytical functions) and the fields that are analyzed. Some of the analytical functions look for single anomalous data points. For example, `max` identifies the maximum value that is seen within a bucket. Others perform some aggregation over the length of the bucket. For example, `mean` calculates the mean of all the data points seen within the bucket.
 
-For more information, see [{{dfeeds-cap}}](ml-ad-run-jobs.md#ml-ad-datafeeds), [Buckets](https://www.elastic.co/guide/en/machine-learning/current/ml-buckets.html), and [*Function reference*](ml-functions.md).
+For more information, see [{{dfeeds-cap}}](ml-ad-run-jobs.md#ml-ad-datafeeds), [Buckets](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-create-job), and [*Function reference*](ml-functions.md).
 
 ::::
 
@@ -317,7 +317,7 @@ If you’re now thinking about where {{anomaly-detect}} can be most impactful fo
 2. It should be information that contains key performance indicators for the health, security, or success of your business or system. The better you know the data, the quicker you will be able to create jobs that generate useful insights.
 3. Ideally, the data is located in {{es}} and you can therefore create a {{dfeed}} that retrieves data in real time.  If your data is outside of {{es}}, you cannot use {{kib}} to create your jobs and you cannot use {{dfeeds}}.
 
-In general, it is a good idea to start with single metric {{anomaly-jobs}} for your key performance indicators. After you examine these simple analysis results, you will have a better idea of what the influencers might be. You can create multi-metric jobs and split the data or create more complex analysis functions as necessary. For examples of more complicated configuration options, see [Examples](https://www.elastic.co/guide/en/machine-learning/current/anomaly-examples.html).
+In general, it is a good idea to start with single metric {{anomaly-jobs}} for your key performance indicators. After you examine these simple analysis results, you will have a better idea of what the influencers might be. You can create multi-metric jobs and split the data or create more complex analysis functions as necessary. For examples of more complicated configuration options, see [Examples](https://www.elastic.co/guide/en/machine-learning/current/anomaly-how-tos.html).
 
 If you want to find more sample jobs, see [Supplied configurations](ootb-ml-jobs.md). In particular, there are sample jobs for [Apache](https://www.elastic.co/guide/en/machine-learning/current/ootb-ml-jobs-apache.html) and [Nginx](https://www.elastic.co/guide/en/machine-learning/current/ootb-ml-jobs-nginx.html) that are quite similar to the examples in this tutorial.
 

@@ -168,7 +168,7 @@ When the aggregation interval of the {{dfeed}} and the bucket span of the job do
 
 ### Calendars and filters are visible in all {{kib}} spaces [ml-space-limitations]
 
-[Spaces](../../../deploy-manage/manage-spaces.md) enable you to organize your {{anomaly-jobs}} in {{kib}} and to see only the jobs and other saved objects that belong to your space. However, this limited scope does not apply to [calendars](https://www.elastic.co/guide/en/machine-learning/current/ml-calendars.html) and [filters](https://www.elastic.co/guide/en/machine-learning/current/ml-rules.html); they are visible in all spaces.
+[Spaces](../../../deploy-manage/manage-spaces.md) enable you to organize your {{anomaly-jobs}} in {{kib}} and to see only the jobs and other saved objects that belong to your space. However, this limited scope does not apply to [calendars](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-calendars) and [filters](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-rules); they are visible in all spaces.
 
 ### Rollup indices are not supported in {{kib}} [ml-rollup-limitations]
 

@@ -12,6 +12,6 @@ The exported file contains configuration details; it does not contain the {{ml}}
 
 There are some additional actions that you must take before you can successfully import and run your jobs:
 
-1. The {{kib}} [{{data-sources}}](https://www.elastic.co/guide/en/kibana/current/index-patterns.html) that are used by {{anomaly-detect}} {dfeeds} and {{dfanalytics}} source indices must exist; otherwise, the import fails.
+1. The {{kib}} [{{data-sources}}](https://www.elastic.co/guide/en/kibana/current/data-views.html) that are used by {{anomaly-detect}} {dfeeds} and {{dfanalytics}} source indices must exist; otherwise, the import fails.
 2. If your {{anomaly-jobs}} use [custom rules](ml-configuring-detector-custom-rules.md) with filter lists, the filter lists must exist; otherwise, the import fails. To create filter lists, use {{kib}} or the [create filters API](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-put-filter.html).
-3. If your {{anomaly-jobs}} were associated with [calendars](https://www.elastic.co/guide/en/machine-learning/current/ml-calendars.html), you must create the calendar in the new environment and add your imported jobs to the calendar. Use {{kib}} or the [create calendars](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-put-calendar.html), [add events to calendar](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-post-calendar-event.html), and [add jobs to calendar](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-put-calendar-job.html) APIs.
+3. If your {{anomaly-jobs}} were associated with [calendars](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-calendars), you must create the calendar in the new environment and add your imported jobs to the calendar. Use {{kib}} or the [create calendars](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-put-calendar.html), [add events to calendar](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-post-calendar-event.html), and [add jobs to calendar](https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-put-calendar-job.html) APIs.
@@ -6,7 +6,7 @@ mapped_pages:
 
 # Supplied configurations [ootb-ml-jobs]
 
-{{anomaly-jobs-cap}} contain the configuration information and metadata necessary to perform an analytics task. {{kib}} can recognize certain types of data and provide specialized wizards for that context. This page lists the categories of the {{anomaly-jobs}} that are ready to use via {{kib}} in **Machine learning**. Refer to [Create {{anomaly-jobs}}](https://www.elastic.co/guide/en/machine-learning/current/create-jobs.html) to learn more about creating a job by using supplied configurations. Logs and Metrics supplied configurations are available and can be created via the related solution UI in {{kib}}.
+{{anomaly-jobs-cap}} contain the configuration information and metadata necessary to perform an analytics task. {{kib}} can recognize certain types of data and provide specialized wizards for that context. This page lists the categories of the {{anomaly-jobs}} that are ready to use via {{kib}} in **Machine learning**. Refer to [Create {{anomaly-jobs}}](https://www.elastic.co/guide/en/machine-learning/current/ml-ad-run-jobs.html#ml-ad-create-job) to learn more about creating a job by using supplied configurations. Logs and Metrics supplied configurations are available and can be created via the related solution UI in {{kib}}.
 
 * [Apache](https://www.elastic.co/guide/en/machine-learning/current/ootb-ml-jobs-apache.html)
 * [APM](https://www.elastic.co/guide/en/machine-learning/current/ootb-ml-jobs-apm.html)

@@ -533,7 +533,7 @@ You can also see the {{feat-imp}} values for each individual prediction in the f
 
 In {{kib}}, the decision path shows the relative impact of each feature on the probability of the prediction. The features with the most significant positive or negative impact appear at the top of the decision plot. Thus in this example, the features related to flight time and distance had the most significant influence on the probability value for this prediction. This type of information can help you to understand how models arrive at their predictions. It can also indicate which aspects of your data set are most influential or least useful when you are training and tuning your model.
 
-If you do not use {{kib}}, you can see the summarized {{feat-imp}} values by using the [get trained model API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-inference.html) and the individual values by searching the destination index.
+If you do not use {{kib}}, you can see the summarized {{feat-imp}} values by using the [get trained model API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-trained-models.html) and the individual values by searching the destination index.
 
 ::::{dropdown} API example
 ```console