review updates

docs/en/observability/monitor-nginx-ml.asciidoc

@@ -2,7 +2,7 @@
 [[monitor-nginx-ml]]
 == Part 5: Find anomalies in your nginx access logs
 
-The {integrations-docs}/nginx[Nginx Elastic integration] provides built in machine learning (ML) jobs to help find unusual activity in your nginx access logs.
+The {integrations-docs}/nginx[Nginx Elastic integration] to help find unusual activity in your nginx access logs.
 Monitoring anomalies in your access logs helps you detect:
 
 * security threats
@@ -17,21 +17,21 @@ Monitoring anomalies in your access logs helps you detect:
 The nginx ML module provides the following anomaly detection jobs:
 
 [[horizontal]]
-Low request rates (low_request_rate_nginx):: Uses the {ml-docs}/ml-count-functions.html#ml-count[`low_count`] function to detect abnormally low request rates. Abnormally low request rates might indicate network issues or other issues are preventing requests from reaching the server.
-Unusual source IPs - high request rates (`source_ip_request_rate_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-count[`hight_count`] function to detect abnormally high request rates from individual IP addresses. Many requests from a single IP or small group of IPs might indicate something malicious like a DDoS attack where many requests are sent to overwhelm the server and make it unavailable to users.
+Low request rates (`low_request_rate_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-count[`low_count`] function to detect abnormally low request rates. Abnormally low request rates might indicate that network issues or other issues are preventing requests from reaching the server.
+Unusual source IPs - high request rates (`source_ip_request_rate_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-count[`hight_count`] function to detect abnormally high request rates from individual IP addresses. Many requests from a single IP or small group of IPs might indicate something malicious like a DDoS attack where a large number of requests are sent to overwhelm the server and make it unavailable to users.
 Unusual source IPs - high distinct count of URLs (`source_ip_url_count_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-distinct-count[`high_distinct_count`] function to detect individual IP addresses accessing abnormally high numbers of unique URLs. A single IP accessing many unique URLs might indicate something malicious like web scraping or an attempt to find sensitive data or vulnerabilities.
 Unusual status code rates (`status_code_rate_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-count[`count`] function to detect abnormal status code rates. A high rate of status codes could indicate problems with broken links, bad URLs, or unauthorized access attempts. A high rate of status codes could also point to server issues like limited resources or bugs in your code.
 Unusual visitor rates (`visitor_rate_nginx`):: Uses the {ml-docs}/ml-count-functions.html#ml-nonzero-count[`non_zero_count`] function to detect abnormal visitor rates. High visitor rates could indicate something malicious like a DDoS attack.
 Low visitor rates could indicate issues with access to the server.
 
-Refer to {integrations-docs}/nginx#ml-modules[Nginx integration ML modules] for more on the jobs and the ML module manifest.
+Refer to {integrations-docs}/nginx#ml-modules[nginx integration ML modules] for more on the jobs and the ML module manifest.
 
 [discrete]
 [[monitor-nginx-ml-prereqs]]
 === Before you begin
 
 Verify that your environment is set up properly to use the {ml-features}.
-If the {es} {security-features} are enabled, you need a user with permissions to manage {anomaly-jobs}.
+If {es} {security-features} are enabled, you need a user with permissions to manage {anomaly-jobs}.
 Refer to {ml-docs}/setup.html[Set up ML features].
 
 [discrete]

docs/en/observability/monitor-nginx.asciidoc

@@ -9,7 +9,7 @@
 Return to this tutorial after you've learned the basics.
 ****
 
-Use the {integrations-docs}/nginx[Nginx Elastic integration] and the {agent} to collect valuable metrics and logs from your nginx instances. Then, use built-in dashboards and tools like Logs Explorer in {kib} allow you to visualize and monitor your nginx data from one place. This data provides valuable insight into your nginx instances—for example:
+Use the {integrations-docs}/nginx[nginx Elastic integration] and the {agent} to collect valuable metrics and logs from your nginx instances. Then, use built-in dashboards and tools like Logs Explorer in {kib} to visualize and monitor your nginx data from one place. This data provides valuable insight into your nginx instances—for example:
 
 * A spike in error logs for a certain resource may mean you have a deleted resource that is still needed.
 * Access logs can show when a service's peak times are, and, from this, when it might be best to perform things like maintenance.
@@ -199,7 +199,7 @@ To open the nginx dashboard:
 The *Metrics Nginx overview* shows visual representations of total requests, processed requests, heartbeat/up, active connections, reading/writing/waiting rates, request rate, accepts and handled rates, and drops rate.
 
 [role="screenshot"]
-image::images/nginx-metrics-dashboard.png[Nginx metrics dashboard, 75%]
+image::images/nginx-metrics-dashboard.png[nginx metrics dashboard, 75%]
 
 [discrete]
 [[monitor-nginx-explore-logs]]
@@ -229,7 +229,7 @@ image::images/nginx-logs-explorer.png[Logs Explorer showing nginx error logs]
 
 [discrete]
 [[monitor-nginx-logs-dashboard]]
-==== Nginx logs dashboards
+==== nginx logs dashboards
 
 The nginx integration has built-in dashboards that show the full picture of your nginx logs in one place.
 To open the nginx dashboards: