[BUG][Serverless][8.15 & 8.16] Refresh the Optimizing anomaly results topic

docs/advanced-entity-analytics/tune-anomaly-results.asciidoc

@@ -16,40 +16,39 @@ you can filter out the unwanted results.
 For example, to filter out results from a housekeeping process, named
 `maintenanceservice.exe`, that only executes occasionally you need to:
 
-. <<create-fiter-list>>
+. <<create-filter-list>>
 . <<add-job-filter>>
 . <<clone-job, Clone and rerun the job>> (optional)
 
 [float]
-[[create-fiter-list]]
+[[create-filter-list]]
+//Make sure that fixing this typo doesn't affect any other references in the Security docset and elsewhere.
 === Create a filter list
 
-. Go to *Machine Learning* -> *Anomaly Detection* -> *Settings*.
-. Click *Filter Lists* and then *New*.
-+
-The *Create new filter list* pane is displayed.
-. Enter a filter list ID.
-. Enter a description for the filter list (optional).
-. Click *Add item*.
-. In the *Items* textbox, enter the name of the process for which you want to
-filter out anomaly results (`maintenanceservice.exe` in our example).
+. To begin creating a new filter, go to **Kibana**, then **Machine Learning** -> **Anomaly Detection** -> **Settings**.
+. In the **Filter Lists** section, click **Create**. 
+. On the Create new filter list page, complete the following:
+.. Enter an ID to name the filter list.
+.. (Optional) Provide a description for the filter list.
+.. Specify the processes that you want to filter out from anomaly results. To do this, click *Add item*, then enter processes names in the text box. In the example below, the `maintenanceservice.exe` process is being added to a filter list that specifies processes to filter out from anomaly results.
+
 +
 [role="screenshot"]
 image::filter-add-item.png[]
-. Click *Add* and then *Save*.
-+
-The new filter appears in the Filter List and can be added to relevant jobs.
+.. Click *Add* and then *Save*.
+
+The new filter appears on the Filter Lists page, where you can add it to relevant jobs.
 
 [float]
 [[add-job-filter]]
 === Add the filter to the relevant job
 
-. Go to *Machine Learning* -> *Anomaly Detection* -> *Anomaly Explorer*.
-. Navigate to the job results for which the filter is required. If the job results
-are not listed, click *Edit job selection* and select the relevant job.
-. In the *actions* column, click the gear icon and then select _Configure rules_.
+. In Kibana, go to **Machine Learning** -> **Anomaly Detection** -> **Anomaly Explorer**.
+. In the Job selection flyout, select the jobs for which you want to add a filter to. If the jobs don't have results, click **Edit job selection* to select other jobs.  
+. Go Anomalies section of the Anomaly Explorer page and and navigate to the job results for which the filter is required. 
+. In the **Actions** column, click the gear icon, then select **Configure rules**.
 +
-The *Create Rule* window is displayed.
+The Create Rule window displays.
 +
 [role="screenshot"]
 image::rule-scope.png[]
@@ -58,13 +57,13 @@ image::rule-scope.png[]
 .. The _WHEN_ statement for the relevant detector (`process.name` in our
 example).
 .. The _IS IN_ statement.
-.. The filter you created as part of the <<create-fiter-list>> procedure.
+.. The filter you created as part of the <<create-filter-list>> procedure.
 +
-TIP: For more information, see
+TIP: To learn more about creating filters that change the behavior of anomaly detectors, refer to
 {ml-docs}/ml-configuring-detector-custom-rules.html[Customizing detectors with custom rules].
 
-. Click *Save*.
-
+. Click *Save* to save the filter to the job results.
++
 NOTE: Changes to rules only affect new results. All anomalies found by the job
 before the filter was added are still displayed.
 
@@ -78,33 +77,27 @@ must clone and run the cloned job.
 IMPORTANT: Running the cloned job can take some time. Only run the job after you
 have completed all job rule changes.
 
-. Go to *Machine Learning* -> *Anomaly Detection* -> *Job Management*.
-. Navigate to the job for which you configured the rule.
-. Optionally, expand the job row and click *JSON* to verify the configured filter
-appears under `custom rules` in the JSON code.
-. In the *actions* column, click the more (three dots) icon and select _Clone job_.
-+
-The *Configure datafeed* page is displayed.
-. Click *Data Preview* and check the data is displayed without errors.
-. Click *Next* until the *Job details* page is displayed.
-. Enter a Job ID for the cloned job that indicates it is an iteration of the
-original one. For example, append a number or a username to the original job
-name, such as `windows-rare-network-process-2`.
+. In Kibana, go to *Machine Learning* -> *Anomaly Detection* -> *Jobs*.
+. Navigate to the job for which you configured the rule. Optionally, expand the job row and go to the *JSON* tab to verify the configured filter appears under `custom rules` in the JSON code.
+. In the *Actions* column, click the **All actions** menu (**...**), then select **Clone job**. The **Create job: Advanced** page is displays.
+. Click **Next** until you get to the Job details page.
+. Enter a job ID for the cloned job. We recommend creating a name that shows the new job is an iteration of the original one. For example, append a number or a username to the original job name, such as `windows-rare-network-process-2`.
 +
 [role="screenshot"]
 image::cloned-job-details.png[]
-. Click *Next* and check the job validates without errors. You can ignore
-warnings about multiple influencers.
-. Click *Next* and then *Create job*.
+
+. Click **Next** and confirm that the job doesn't return errors. You can ignore warnings about multiple influencers.
+. Click **Next**, then **Create job**.
 +
-The *Start <job name>* window is displayed.
+The *Start <job name>* window displays.
 +
 [role="screenshot"]
 image::start-job-window.png[]
-. Select the point of time from which the job will analyze anomalies.
-. Click *Start*.
+
+. Specify when the job begins to analyze anomalies.
+. Click **Start**.
 +
-After a while, results will start to appear on the *Anomaly Explorer* page.
+Results will eventually appear on the Anomaly Explorer page.
 
 [float]
 [[define-rule-threshold]]
@@ -121,7 +114,7 @@ Depending on your anomaly detection results, you may want to set a
 minimum event count threshold for the `packetbeat_dns_tunneling` job:
 
 
-. Go to *Machine Learning* -> *Anomaly Detection* -> *Anomaly Explorer*.
+. In Kibana, go to **Machine Learning** -> **Anomaly Detection** -> **Anomaly Explorer**.
 . Navigate to the job results for the `packetbeat_dns_tunneling` job. If the 
 job results are not listed, click *Edit job selection* and select 
 `packetbeat_dns_tunneling`.
@@ -138,6 +131,7 @@ image::ml-rule-threshold.png[]
 _WHEN actual IS GREATER THAN <X>_
 +
 Where `<X>` is the threshold above which anomalies are detected.
-. Click *Save*.
-. To apply the new threshold, rerun the job (*Job Management* -> *Actions* ->
-*Start datafeed*).
+. Click **Save**.
+. To apply the new threshold, rerun the job: 
+.. Go to **Anomaly Detection** -> **Jobs**, and find the `packetbeat_dns_tunneling`.   
+.. In the *Actions* column, click the **All actions** menu (**...**), then select **Start datafeed**.