Add links to API and PPl. Add to site.

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

_config.yml

@@ -94,6 +94,9 @@ just_the_docs:
     observability-plugin:
       name: Observability plugin
       nav_fold: true
+    ml-commons-plugin:
+      name: ML Commons plugin
+      nav_fold: true
     monitoring-plugins:
       name: Monitoring plugins
       nav_fold: true

_ml-commons-plugin/api.md

@@ -7,6 +7,15 @@ nav_order: 99
 
 # ML Commons API 
 
+---
+
+#### Table of contents
+- TOC
+{:toc}
+
+
+---
+
 The Machine Learning (ML) commons API lets you create, train, and store machine learning algorithms both synchronously and asynchronously.  
 
 In order to train tasks through the API, three inputs are required. 

_ml-commons-plugin/index.md

@@ -3,18 +3,19 @@ layout: default
 title: About ML Commons
 nav_order: 38
 has_children: false
+has_toc: false
 ---
 
 # ML Commons plugin
 
 ML Commons for OpenSearch eases the development of machine learning features by providing a set of common machine learning (ML) algorithms through transport and REST API calls. Those calls choose the right nodes and resources for each ML request and monitors ML tasks to ensure uptime. This allows you to leverage existing open-source ML algorithms and reduce the effort required to develop new ML features. 
 
-Models trained through the ML Commons plugin support two types of algorithms. 
+Models trained through the ML Commons plugin support two types of algorithms: 
 
-- Model-based algorithms such Kmeans or Linear Regression. To get the best results, make sure you train your model first, then use the model to apply predictions. Linear Regression is only supported 
+- Model-based algorithms such kmeans or Linear Regression. To get the best results, make sure you train your model first, then use the model to apply predictions. Linear Regression is only supported 
 - No-model based algorithm such as RCA. These algorithms can be executed directly through an `Executable` interface. 
 
-Interaction with the ML commons plugin occurs through either the [REST API] or AD and Kmeans PPL commands.
+Interaction with the ML commons plugin occurs through either the [REST API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api) or [AD]({{site.url}}{{site.baseurl}}/ppl/commands#ad) and [kmeans]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/commands#kmeans) PPL commands.
 
 ## Permissions
 
@@ -23,7 +24,9 @@ There are two user roles that can make use of the ML commons plugin.
 - `ml_full_access`: Full access to all ML features, including starting new jobs and reading or deleting models.
 - `ml_readonly_access`: Can only read trained models and statistics relevant to the model's cluster. Cannot start jobs or delete models.
 
-## Quickstart 
+
+
+
 
 
 

index.md

@@ -35,9 +35,11 @@ Component | Purpose
 [KNN]({{site.url}}{{site.baseurl}}/search-plugins/knn/) | Find “nearest neighbors” in your vector data
 [Performance Analyzer]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/) | Monitor and optimize your cluster
 [Anomaly detection]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/) | Identify atypical data and receive automatic notifications
+[ML Commons plugin]({{site.url}}{{site.baseurl}}/ml-commons-plugin/) | Train and execute machine-learning models
 [Asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/) | Run search requests in the background
 [Cross-cluster replication]({{site.url}}{{site.baseurl}}/replication-plugin/index/) | Replicate your data across multiple OpenSearch clusters
 
+
 Most OpenSearch plugins have corresponding OpenSearch Dashboards plugins that provide a convenient, unified user interface.
 
 For specifics around the project, see the [FAQ](https://opensearch.org/faq/).