diff --git a/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md b/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md index 5765cbe243..17b9b27ec8 100644 --- a/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md +++ b/explore-analyze/alerts-cases/alerts/rule-type-index-threshold.md @@ -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. diff --git a/explore-analyze/dashboards/building.md b/explore-analyze/dashboards/building.md index fa5d81ecc5..e80c37b693 100644 --- a/explore-analyze/dashboards/building.md +++ b/explore-analyze/dashboards/building.md @@ -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. :::: diff --git a/explore-analyze/dashboards/drilldowns.md b/explore-analyze/dashboards/drilldowns.md index 96d77d08cc..4afe8a9838 100644 --- a/explore-analyze/dashboards/drilldowns.md +++ b/explore-analyze/dashboards/drilldowns.md @@ -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 "") diff --git a/explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md b/explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md index 4512dcce54..d0ef94a464 100644 --- a/explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md +++ b/explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md @@ -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] diff --git a/explore-analyze/machine-learning/anomaly-detection/geographic-anomalies.md b/explore-analyze/machine-learning/anomaly-detection/geographic-anomalies.md index 279a310f2b..aaf0dfc132 100644 --- a/explore-analyze/machine-learning/anomaly-detection/geographic-anomalies.md +++ b/explore-analyze/machine-learning/anomaly-detection/geographic-anomalies.md @@ -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] diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md index 4c86917cc9..b7dabe3b36 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-ad-run-jobs.md @@ -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] diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md index 65a1036b5a..1785cb20d6 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md @@ -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. diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-limitations.md b/explore-analyze/machine-learning/anomaly-detection/ml-limitations.md index d74bfed899..cc7dbed20a 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-limitations.md +++ b/explore-analyze/machine-learning/anomaly-detection/ml-limitations.md @@ -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] diff --git a/explore-analyze/machine-learning/anomaly-detection/move-jobs.md b/explore-analyze/machine-learning/anomaly-detection/move-jobs.md index 4d5c5bf46a..73e3f83b8f 100644 --- a/explore-analyze/machine-learning/anomaly-detection/move-jobs.md +++ b/explore-analyze/machine-learning/anomaly-detection/move-jobs.md @@ -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. diff --git a/explore-analyze/machine-learning/anomaly-detection/ootb-ml-jobs.md b/explore-analyze/machine-learning/anomaly-detection/ootb-ml-jobs.md index 3163783a8c..5e31ba7d5b 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ootb-ml-jobs.md +++ b/explore-analyze/machine-learning/anomaly-detection/ootb-ml-jobs.md @@ -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) diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md index 4ae0f76d56..0862a68a3e 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-classification.md @@ -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 diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md index b237b6c567..dc3c1fab95 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-finding-outliers.md @@ -226,7 +226,7 @@ POST _transform/_preview Even though resource utilization is automatically adjusted based on the cluster load, a {{transform}} increases search and indexing load on your cluster while it runs. If you’re experiencing an excessive load, however, you can stop it. :::: - You can start, stop, and manage {{transforms}} in {{kib}}. Alternatively, you can use the [start {{transforms}}](https://www.elastic.co/guide/en/elasticsearch/reference/current/start-data-frame-transform.html) API. + You can start, stop, and manage {{transforms}} in {{kib}}. Alternatively, you can use the [start {{transforms}}](https://www.elastic.co/guide/en/elasticsearch/reference/current/start-transform.html) API. ::::{dropdown} API example ```console @@ -356,7 +356,7 @@ GET weblog-outliers/_search?q="111.237.144.54" Now that you’ve found unusual behavior in the sample data set, consider how you might apply these steps to other data sets. If you have data that is already marked up with true outliers, you can determine how well the {{oldetection}} algorithms perform by using the evaluate {{dfanalytics}} API. See [6. Evaluate the results](#ml-outlier-detection-evaluate). ::::{tip} -If you do not want to keep the {{transform}} and the {{dfanalytics}} job, you can delete them in {{kib}} or use the [delete {{transform}} API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-data-frame-transform.html) and [delete {{dfanalytics}} job API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-dfanalytics.html). When you delete {{transforms}} and {{dfanalytics}} jobs in {{kib}}, you have the option to also remove the destination indices and {{data-sources}}. +If you do not want to keep the {{transform}} and the {{dfanalytics}} job, you can delete them in {{kib}} or use the [delete {{transform}} API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-transform.html) and [delete {{dfanalytics}} job API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-dfanalytics.html). When you delete {{transforms}} and {{dfanalytics}} jobs in {{kib}}, you have the option to also remove the destination indices and {{data-sources}}. :::: ## Further reading [outlier-detection-reading] diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md index 6dab15d005..39bd29ecc8 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-dfa-regression.md @@ -437,7 +437,7 @@ You can also see the {{feat-imp}} values for each individual prediction in the f The decision path starts at a baseline, which is the average of the predictions for all the data points in the training data set. From there, the feature importance values are added to the decision path until it arrives at its final prediction. The features with the most significant positive or negative impact appear at the top. Thus in this example, the features related to the flight distance had the most significant influence on this particular predicted flight delay. 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 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 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 diff --git a/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md b/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md index f6463c4642..d41d605034 100644 --- a/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md +++ b/explore-analyze/machine-learning/data-frame-analytics/ml-feature-importance.md @@ -9,7 +9,7 @@ mapped_pages: The purpose of {{feat-imp}} is to help you determine whether the predictions are sensible. Is the relationship between the dependent variable and the important features supported by your domain knowledge? The lessons you learn about the importance of specific features might also affect your decision to include them in future iterations of your trained model. -You can see the average magnitude of the {{feat-imp}} values for each field across all the training data in {{kib}} or by using the [get trained model API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-inference.html). For example, {{kib}} shows the total feature importance for each field in {{regression}} or binary {{classanalysis}} results as follows: +You can see the average magnitude of the {{feat-imp}} values for each field across all the training data in {{kib}} or by using the [get trained model API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-trained-models.html). For example, {{kib}} shows the total feature importance for each field in {{regression}} or binary {{classanalysis}} results as follows: :::{image} ../../../images/machine-learning-flights-regression-total-importance.jpg :alt: Total {{feat-imp}} values for a {{regression}} {dfanalytics-job} in {kib} diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md b/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md index b24ee042d4..4c5b4493ad 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-deploy-model.md @@ -19,7 +19,7 @@ You can optimize your deplyoment for typical use cases, such as search and inges Each deployment will be fine-tuned automatically based on its specific purpose you choose. ::::{note} -Since eland uses APIs to deploy the models, you cannot see the models in {{kib}} until the saved objects are synchronized. You can follow the prompts in {{kib}}, wait for automatic synchronization, or use the [sync {{ml}} saved objects API](https://www.elastic.co/guide/en/kibana/current/machine-learning-api-sync.html). +Since eland uses APIs to deploy the models, you cannot see the models in {{kib}} until the saved objects are synchronized. You can follow the prompts in {{kib}}, wait for automatic synchronization, or use the [sync {{ml}} saved objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml). :::: You can define the resource usage level of the NLP model during model deployment. The resource usage levels behave differently depending on [adaptive resources](ml-nlp-auto-scale.md#nlp-model-adaptive-resources) being enabled or disabled. When adaptive resources are disabled but {{ml}} autoscaling is enabled, vCPU usage of Cloud deployments derived from the Cloud console and functions as follows: diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md index 8366adb606..63c2fa9f00 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-ner-example.md @@ -44,7 +44,7 @@ You need to provide an administrator username and its password and replace the ` Since the `--start` option is used at the end of the Eland import command, {{es}} deploys the model ready to use. If you have multiple models and want to select which model to deploy, you can use the **{{ml-app}} > Model Management** user interface in {{kib}} to manage the starting and stopping of models. -Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/guide/en/kibana/current/ml-sync.html). +Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml). ## Test the NER model [ex-ner-test] @@ -56,7 +56,7 @@ Deployed models can be evaluated in {{kib}} under **{{ml-app}}** > **Trained Mod ::: ::::{dropdown} **Test the model by using the _infer API** -You can also evaluate your models by using the [_infer API](https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model-deployment.html). In the following request, `text_field` is the field name where the model expects to find the input, as defined in the model configuration. By default, if the model was uploaded via Eland, the input field is `text_field`. +You can also evaluate your models by using the [_infer API](https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html). In the following request, `text_field` is the field name where the model expects to find the input, as defined in the model configuration. By default, if the model was uploaded via Eland, the input field is `text_field`. ```js POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/_infer diff --git a/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md b/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md index 0641d38dc7..4c2a1d6825 100644 --- a/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md +++ b/explore-analyze/machine-learning/nlp/ml-nlp-text-emb-vector-search-example.md @@ -48,7 +48,7 @@ You need to provide an administrator username and password and replace the `$CLO Since the `--start` option is used at the end of the Eland import command, {{es}} deploys the model ready to use. If you have multiple models and want to select which model to deploy, you can use the **{{ml-app}} > Model Management** user interface in {{kib}} to manage the starting and stopping of models. -Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/guide/en/kibana/current/ml-sync.html). +Go to the **{{ml-app}} > Trained Models** page and synchronize your trained models. A warning message is displayed at the top of the page that says *"ML job and trained model synchronization required"*. Follow the link to *"Synchronize your jobs and trained models."* Then click **Synchronize**. You can also wait for the automatic synchronization that occurs in every hour, or use the [sync {{ml}} objects API](https://www.elastic.co/docs/api/doc/kibana/v8/group/endpoint-ml). ## Test the text embedding model [ex-text-emb-test] @@ -60,7 +60,7 @@ Deployed models can be evaluated in {{kib}} under **{{ml-app}}** > **Trained Mod ::: ::::{dropdown} **Test the model by using the _infer API** -You can also evaluate your models by using the [_infer API](https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model-deployment.html). In the following request, `text_field` is the field name where the model expects to find the input, as defined in the model configuration. By default, if the model was uploaded via Eland, the input field is `text_field`. +You can also evaluate your models by using the [_infer API](https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html). In the following request, `text_field` is the field name where the model expects to find the input, as defined in the model configuration. By default, if the model was uploaded via Eland, the input field is `text_field`. ```js POST /_ml/trained_models/sentence-transformers__msmarco-minilm-l-12-v3/_infer diff --git a/explore-analyze/query-filter.md b/explore-analyze/query-filter.md index ef9a56d5ba..c03d2d2d35 100644 --- a/explore-analyze/query-filter.md +++ b/explore-analyze/query-filter.md @@ -13,7 +13,7 @@ You’ll use a combination of an API endpoint and a query language to interact w - Elasticsearch provides a number of [query languages](/explore-analyze/query-filter/languages.md). From Query DSL to the newest ES|QL, find the one that's most appropriate for you. -- You can call Elasticsearch's REST APIs by submitting requests directly from the command line or through the Dev Tools [Console](/explore-analyze/query-filter/tools/console.md) in {{kib}}. From your applications, you can use a [client](https://www.elastic.co/guide/en/elasticsearch/client/index.md) in your programming language of choice. +- You can call Elasticsearch's REST APIs by submitting requests directly from the command line or through the Dev Tools [Console](/explore-analyze/query-filter/tools/console.md) in {{kib}}. From your applications, you can use a [client](https://www.elastic.co/guide/en/elasticsearch/client/index.html) in your programming language of choice. - A number of [tools](/explore-analyze/query-filter/tools.md) are available for you to save, debug, and optimize your queries. diff --git a/explore-analyze/query-filter/languages/eql.md b/explore-analyze/query-filter/languages/eql.md index 36fef11159..1e6825f588 100644 --- a/explore-analyze/query-filter/languages/eql.md +++ b/explore-analyze/query-filter/languages/eql.md @@ -1131,7 +1131,7 @@ The async search continues to run in the background without blocking other reque } ``` -To check the progress of an async search, use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/eql-search-api.html) with the search ID. Specify how long you’d like for complete results in the `wait_for_completion_timeout` parameter. +To check the progress of an async search, use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-async-eql-search-api.html) with the search ID. Specify how long you’d like for complete results in the `wait_for_completion_timeout` parameter. ```console GET /_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=?wait_for_completion_timeout=2s @@ -1182,13 +1182,13 @@ GET /my-data-stream/_eql/search } ``` -You can use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/eql-search-api.html)'s `keep_alive` parameter to later change the retention period. The new retention period starts after the get request runs. +You can use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-async-eql-search-api.html)'s `keep_alive` parameter to later change the retention period. The new retention period starts after the get request runs. ```console GET /_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=?keep_alive=5d ``` -Use the [delete async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/eql-search-api.html) to manually delete an async EQL search before the `keep_alive` period ends. If the search is still ongoing, {{es}} cancels the search request. +Use the [delete async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-async-eql-search-api.html) to manually delete an async EQL search before the `keep_alive` period ends. If the search is still ongoing, {{es}} cancels the search request. ```console DELETE /_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE= @@ -1223,7 +1223,7 @@ The response includes a search ID. `is_partial` and `is_running` are `false`, in } ``` -Use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/eql-search-api.html) to get the same results later: +Use the [get async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-async-eql-search-api.html) to get the same results later: ```console GET /_eql/search/FjlmbndxNmJjU0RPdExBTGg0elNOOEEaQk9xSjJBQzBRMldZa1VVQ2pPa01YUToxMDY= @@ -1233,7 +1233,7 @@ Saved synchronous searches are still subject to the `keep_alive` parameter’s r You can also check only the status of the saved synchronous search without results by using [get async EQL status API](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-async-eql-status-api.html). -You can also manually delete saved synchronous searches using the [delete async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/eql-search-api.html). +You can also manually delete saved synchronous searches using the [delete async EQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/delete-async-eql-search-api.html). ## Run an EQL search across clusters [run-eql-search-across-clusters] diff --git a/explore-analyze/query-filter/languages/sql-index-frozen.md b/explore-analyze/query-filter/languages/sql-index-frozen.md index ff15734deb..c2a6605843 100644 --- a/explore-analyze/query-filter/languages/sql-index-frozen.md +++ b/explore-analyze/query-filter/languages/sql-index-frozen.md @@ -5,7 +5,7 @@ mapped_pages: # Frozen Indices [sql-index-frozen] -By default, Elasticsearch SQL doesn’t search [frozen indices](https://www.elastic.co/guide/en/elasticsearch/reference/current/frozen-indices.html). To search frozen indices, use one of the following features: +By default, Elasticsearch SQL doesn’t search [frozen indices](https://www.elastic.co/guide/en/elasticsearch/reference/current/unfreeze-index-api.html). To search frozen indices, use one of the following features: dedicated configuration parameter : Set to `true` properties `index_include_frozen` in the [SQL search API](https://www.elastic.co/guide/en/elasticsearch/reference/current/sql-search-api.html) or `index.include.frozen` in the drivers to include frozen indices. diff --git a/explore-analyze/transforms/ecommerce-transforms.md b/explore-analyze/transforms/ecommerce-transforms.md index f7f7c3774a..5f679fcbad 100644 --- a/explore-analyze/transforms/ecommerce-transforms.md +++ b/explore-analyze/transforms/ecommerce-transforms.md @@ -5,7 +5,7 @@ mapped_pages: # Tutorial: Transforming the eCommerce sample data [ecommerce-transforms] -[{{transforms-cap}}](../transforms.md) enable you to retrieve information from an {{es}} index, transform it, and store it in another index. Let’s use the [{{kib}} sample data](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) to demonstrate how you can pivot and summarize your data with {{transforms}}. +[{{transforms-cap}}](../transforms.md) enable you to retrieve information from an {{es}} index, transform it, and store it in another index. Let’s use the [{{kib}} sample data](https://www.elastic.co/guide/en/kibana/current/get-started.html) to demonstrate how you can pivot and summarize your data with {{transforms}}. 1. Verify that your environment is set up properly to use {{transforms}}. If the {{es}} {security-features} are enabled, to complete this tutorial you need a user that has authority to preview and create {{transforms}}. You must also have specific index privileges for the source and destination indices. See [Setup](transform-setup.md). 2. Choose your *source index*. diff --git a/explore-analyze/transforms/transform-examples.md b/explore-analyze/transforms/transform-examples.md index 9292838f76..cab2c0ad66 100644 --- a/explore-analyze/transforms/transform-examples.md +++ b/explore-analyze/transforms/transform-examples.md @@ -6,7 +6,7 @@ mapped_pages: # Examples [transform-examples] -These examples demonstrate how to use {{transforms}} to derive useful insights from your data. All the examples use one of the [{{kib}} sample datasets](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html). For a more detailed, step-by-step example, see [Tutorial: Transforming the eCommerce sample data](ecommerce-transforms.md). +These examples demonstrate how to use {{transforms}} to derive useful insights from your data. All the examples use one of the [{{kib}} sample datasets](https://www.elastic.co/guide/en/kibana/current/get-started.html). For a more detailed, step-by-step example, see [Tutorial: Transforming the eCommerce sample data](ecommerce-transforms.md). * [Finding your best customers](#example-best-customers) * [Finding air carriers with the most delays](#example-airline) diff --git a/explore-analyze/visualize.md b/explore-analyze/visualize.md index 7fc0ee2c15..1c36bbf1e8 100644 --- a/explore-analyze/visualize.md +++ b/explore-analyze/visualize.md @@ -15,7 +15,7 @@ $$$panels-editors$$$ | --- | --- | --- | | Visualizations | | | | [Lens](visualize/lens.md) | The default editor for creating powerful [charts](visualize/supported-chart-types.md) in {{kib}} | -| [ES|QL](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-kibana.md) | Create visualizations from ES|QL queries | +| [ES|QL](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-kibana.html) | Create visualizations from ES|QL queries | | [Maps](visualize/maps.md) | Create beautiful displays of your geographical data | | [Field statistics](visualize/field-statistics.md) | Add a field statistics view of your data to your dashboards | | [Custom visualizations](visualize/custom-visualizations-with-vega.md) | Use Vega to create new types of visualizations | @@ -29,9 +29,9 @@ $$$panels-editors$$$ | [Single metric viewer](machine-learning/machine-learning-in-kibana/xpack-ml-anomalies.md) | Display an anomaly chart from the **Single Metric Viewer** | | [Change point detection](machine-learning/machine-learning-in-kibana/xpack-ml-aiops.md#change-point-detection) | Display a chart to visualize change points in your data | | Observability | | | -| [SLO overview](https://www.elastic.co/guide/en/observability/current/slo.md) | Visualize a selected SLO’s health, including name, current SLI value, target, and status | -| [SLO Alerts](https://www.elastic.co/guide/en/observability/current/slo.md) | Visualize one or more SLO alerts, including status, rule name, duration, and reason. In addition, configure and update alerts, or create cases directly from the panel | -| [SLO Error Budget](https://www.elastic.co/guide/en/observability/current/slo.md) | Visualize the consumption of your SLO’s error budget | +| [SLO overview](https://www.elastic.co/guide/en/observability/current/slo.html) | Visualize a selected SLO’s health, including name, current SLI value, target, and status | +| [SLO Alerts](https://www.elastic.co/guide/en/observability/current/slo.html) | Visualize one or more SLO alerts, including status, rule name, duration, and reason. In addition, configure and update alerts, or create cases directly from the panel | +| [SLO Error Budget](https://www.elastic.co/guide/en/observability/current/slo.html) | Visualize the consumption of your SLO’s error budget | | Legacy | | | | [Log stream](https://www.elastic.co/guide/en/kibana/current/observability.html#logs-app) (deprecated) | Display a table of live streaming logs | | [Aggregation based](visualize/legacy-editors/aggregation-based.md) | While these panel types are still available, we recommend to use [Lens](visualize/lens.md) | diff --git a/explore-analyze/visualize/canvas.md b/explore-analyze/visualize/canvas.md index cfbd3c0054..2bb6298e74 100644 --- a/explore-analyze/visualize/canvas.md +++ b/explore-analyze/visualize/canvas.md @@ -31,7 +31,7 @@ A *workpad* provides you with a space where you can build presentations of your To create workpads, you must meet the minimum requirements. * If you need to set up {{kib}}, use [our free trial](https://www.elastic.co/cloud/elasticsearch-service/signup?baymax=docs-body&elektra=docs). -* Make sure you 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). +* Make sure you 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). * Have an understanding of [{{es}} documents and indices](../../manage-data/data-store/index-basics.md). * Make sure you have sufficient privileges to create and save workpads. When the read-only indicator appears, you have insufficient privileges, and the options to create and save workpads are unavailable. For more information, refer to [Granting access to {{kib}}](../../deploy-manage/users-roles/cluster-or-deployment-auth/built-in-roles.md). diff --git a/explore-analyze/visualize/canvas/canvas-tutorial.md b/explore-analyze/visualize/canvas/canvas-tutorial.md index f22296e010..436056ca78 100644 --- a/explore-analyze/visualize/canvas/canvas-tutorial.md +++ b/explore-analyze/visualize/canvas/canvas-tutorial.md @@ -133,6 +133,6 @@ Now that you know the basics, you’re ready to explore on your own. Here are some things to try: -* Play with the [sample Canvas workpads](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html). +* Play with the [sample Canvas workpads](https://www.elastic.co/guide/en/kibana/current/get-started.html). * Build presentations of your own data with [workpads](../canvas.md#create-workpads). * Deep dive into the [expression language and functions](canvas-function-reference.md) that drive **Canvas**. diff --git a/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md b/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md index 03255f8df1..15687bac13 100644 --- a/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md +++ b/explore-analyze/visualize/maps/maps-search-across-multiple-indices.md @@ -19,7 +19,7 @@ One strategy for eliminating unintentional empty layers from a cross index searc Add [_index](https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-index-field.html) to your search to include documents from indices that do not contain a search field. -For example, suppose you have a vector layer showing the `kibana_sample_data_logs` documents and another vector layer with `kibana_sample_data_flights` documents. (See [adding sample data](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) to install the `kibana_sample_data_logs` and `kibana_sample_data_flights` indices.) +For example, suppose you have a vector layer showing the `kibana_sample_data_logs` documents and another vector layer with `kibana_sample_data_flights` documents. (See [adding sample data](https://www.elastic.co/guide/en/kibana/current/get-started.html) to install the `kibana_sample_data_logs` and `kibana_sample_data_flights` indices.) If you query for diff --git a/explore-analyze/visualize/maps/vector-style.md b/explore-analyze/visualize/maps/vector-style.md index ffa6a27703..f3f7c9d50a 100644 --- a/explore-analyze/visualize/maps/vector-style.md +++ b/explore-analyze/visualize/maps/vector-style.md @@ -12,7 +12,7 @@ When styling a vector layer, you can customize your data by property, such as si Use static styling to specify a constant value for a style property. -This image shows an example of static styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) data set. The **kibana_sample_data_logs** layer uses static styling for all properties. +This image shows an example of static styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/get-started.html) data set. The **kibana_sample_data_logs** layer uses static styling for all properties. :::{image} ../../../images/kibana-vector_style_static.png :alt: vector style static @@ -24,7 +24,7 @@ This image shows an example of static styling using the [Kibana sample web logs] Use data driven styling to symbolize features by property values. To enable data driven styling for a style property, change the selected value from **Fixed** or **Solid** to **By value**. -This image shows an example of data driven styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) data set. The **kibana_sample_data_logs** layer uses data driven styling for fill color and symbol size style properties. +This image shows an example of data driven styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/get-started.html) data set. The **kibana_sample_data_logs** layer uses data driven styling for fill color and symbol size style properties. * The `hour_of_day` property determines the fill color for each feature based on where the value fits on a linear scale. Light green circles symbolize documents that occur earlier in the day, and dark green circles symbolize documents that occur later in the day. * The `bytes` property determines the size of each symbol based on where the value fits on a linear scale. Smaller circles symbolize documents with smaller payloads, and larger circles symbolize documents with larger payloads. @@ -39,7 +39,7 @@ This image shows an example of data driven styling using the [Kibana sample web Quantitative data driven styling symbolizes features from a range of numeric property values. -Property values are fit from the domain range to the style range on a linear scale. For example, let’s symbolize [Kibana sample web log](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) documents by size. The sample web logs `bytes` field ranges from 0 to 18,000. This is the domain range. The smallest feature has a symbol radius of 1, and the largest feature has a symbol radius of 24. This is the style range. The `bytes` property value for each feature will fit on a linear scale from the range of 0 to 18,000 to the style range of 1 to 24. +Property values are fit from the domain range to the style range on a linear scale. For example, let’s symbolize [Kibana sample web log](https://www.elastic.co/guide/en/kibana/current/get-started.html) documents by size. The sample web logs `bytes` field ranges from 0 to 18,000. This is the domain range. The smallest feature has a symbol radius of 1, and the largest feature has a symbol radius of 24. This is the style range. The `bytes` property value for each feature will fit on a linear scale from the range of 0 to 18,000 to the style range of 1 to 24. For color styles, values are fit from the domain range to the color ramp with one of the following: @@ -71,7 +71,7 @@ Qualitative data driven styling is available for the following styling propertie * **Label color** * **Label border color** -This image shows an example of quantitative data driven styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) data set. The `machine.os.keyword` property determines the color of each symbol based on category. +This image shows an example of quantitative data driven styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/get-started.html) data set. The `machine.os.keyword` property determines the color of each symbol based on category. :::{image} ../../../images/kibana-quantitative_data_driven_styling.png :alt: quantitative data driven styling @@ -83,7 +83,7 @@ This image shows an example of quantitative data driven styling using the [Kiban Class styling symbolizes features by class and requires multiple layers. Use [layer filtering](maps-layer-based-filtering.md) to define the class for each layer, and [static styling](#maps-vector-style-static) to symbolize each class. -This image shows an example of class styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/add-sample-data.html) data set. +This image shows an example of class styling using the [Kibana sample web logs](https://www.elastic.co/guide/en/kibana/current/get-started.html) data set. * The **Mac OS requests** layer applies the filter `machine.os : osx` so the layer only contains Mac OS requests. The fill color is a static value of green. * The **Window OS requests** layer applies the filter `machine.os : win*` so the layer only contains Window OS requests. The fill color is a static value of red.