Fix ML links (#3976)

* Fix ML links

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

* Fix style errors

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

* Fix RCF spelling

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

---------

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

_ml-commons-plugin/algorithms.md

@@ -27,7 +27,7 @@ distance_type | enum, such as `EUCLIDEAN`, `COSINE`, or `L1` | The type of measu
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -77,7 +77,7 @@ optimizerType | OptimizerType | The optimizer used in the model. | SIMPLE_SGD
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 
 ### Example
@@ -189,7 +189,7 @@ time_zone | string | The time zone for the `time_field` field. | "UTC"
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -198,9 +198,9 @@ time_zone | string | The time zone for the `time_field` field. | "UTC"
 
 For FIT RCF, you can train the model with historical data and store the trained model in your index. The model will be deserialized and predict new data points when using the Predict API. However, the model in the index will not be refreshed with new data, because the model is fixed in time.
 
-## RCFSummarize
+## RCF Summarize
 
-RCFSummarize is a clustering algorithm based on the Clustering Using REpresentatives (CURE) algorithm. Compared to [k-means](#k-means), which uses random iterations to cluster, RCFSummarize uses a hierarchical clustering technique. The algorithm starts, with a set of randomly selected centroids larger than the centroids' ground truth distribution. During iteration, centroid pairs too close to each other automatically merge. Therefore, the number of centroids (`max_k`) converge to a rational number of clusters that fits ground truth, as opposed to a fixed `k` number of clusters.  
+RCF Summarize is a clustering algorithm based on the Clustering Using Representatives (CURE) algorithm. Compared to [k-means](#k-means), which uses random iterations to cluster, RCF Summarize uses a hierarchical clustering technique. The algorithm starts, with a set of randomly selected centroids larger than the centroids' ground truth distribution. During iteration, centroid pairs too close to each other automatically merge. Therefore, the number of centroids (`max_k`) converge to a rational number of clusters that fits ground truth, as opposed to a fixed `k` number of clusters.  
 
 ### Parameters
 
@@ -211,7 +211,7 @@ RCFSummarize is a clustering algorithm based on the Clustering Using REpresentat
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 * [Train and predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-and-predict)
 
@@ -429,7 +429,7 @@ A classification algorithm, logistic regression models the probability of a disc
 
 ### APIs
 
-* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#training-a-model)
+* [Train]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#train-model)
 * [Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#predict)
 
 ### Example: Train/Predict with Iris data
@@ -651,7 +651,7 @@ PUT /_cluster/settings
 
 ### Parameters
 
-To use the metrics correlation algorthim, include the following parameters.
+To use the metrics correlation algorithm, include the following parameters.
 
 | Parameter | Type | Description | Default value |
 |---|---|---|---|
@@ -661,7 +661,7 @@ metrics | Array | A list of metrics within the time series that can be correlate
 
 The metrics correlation input is an $M$ x $T$ array of metrics data, where M is the number of metrics and T is the length of each individual sequence of metric values. 
 
-When inputting metrics into the algorthim, assume the following:
+When inputting metrics into the algorithm, assume the following:
 
 1. For each metric, the input sequence has the same length, $T$.
 2. All input metrics should have the same corresponding set of timestamps.

_ml-commons-plugin/cluster-settings.md

@@ -39,7 +39,7 @@ plugins.ml_commons.task_dispatch_policy: round_robin
 
 ### Values
 
-- Dafault value: `round_robin`
+- Default value: `round_robin`
 - Value range: `round_robin` or `least_load`
 
 ## Set number of ML tasks per node
@@ -74,7 +74,7 @@ plugins.ml_commons.max_model_on_node: 10
 
 ## Set sync job intervals 
 
-When returning runtime information with the [Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#returning-model-profile-information), ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to `0`, ML Commons immediately stops sync-up jobs.
+When returning runtime information with the [Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#profile), ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to `0`, ML Commons immediately stops sync-up jobs.
 
 
 ### Setting

_ml-commons-plugin/index.md

@@ -12,7 +12,7 @@ ML Commons for OpenSearch eases the development of machine learning features by
 
 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}}/search-plugins/sql/ppl/functions#ad) and [`kmeans`]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/functions#kmeans) Piped Processing Language (PPL) commands.
 
-Models [trained]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#training-a-model) through the ML Commons plugin support model-based algorithms such as kmeans. After you've trained a model enough so that it meets your precision requirements, you can apply the model to [predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#predict) new data safely. 
+Models [trained]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train-model) through the ML Commons plugin support model-based algorithms such as kmeans. After you've trained a model enough so that it meets your precision requirements, you can apply the model to [predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#predict) new data safely. 
 
 Should you not want to use a model, you can use the [Train and Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train-and-predict) API to test your model without having to evaluate the model's performance.
 

version-history.md

@@ -17,7 +17,7 @@ OpenSearch version | Release highlights | Release date
 [2.4.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.4.0.md) | Includes Windows support, Point-in-time search, custom k-NN filtering, xy_point and xy_shape field types for Cartesian coordinates, GeoHex grid aggregation, and resilience enhancements, including search backpressure. In OpenSearch Dashboards, this release adds snapshot restore functionality, multiple authentication, and aggregate view of saved objects. This release includes the following experimental features: [searchable snapshots]({{site.url}}{{site.baseurl}}/opensearch/snapshots/searchable_snapshot/), [Compare Search Results]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/), [multiple data sources in OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/dashboards/discover/multi-data-sources/), a new [Model Serving Framework]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework/) in ML Commons, a new [Neural Search plugin]({{site.url}}{{site.baseurl}}/neural-search-plugin/index/) that supports semantic search, and a new [Security Analytics]({{site.url}}{{site.baseurl}}/security-analytics/index/) plugin to analyze security logs. For a full list of release highlights, see the Release Notes. | 15 November 2022
 [2.3.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.3.0.md) | This release includes the following experimental features: [segment replication]({{site.url}}{{site.baseurl}}/opensearch/segment-replication/), [remote-backed storage]({{site.url}}{{site.baseurl}}/opensearch/remote/), and [drag and drop]({{site.url}}{{site.baseurl}}/dashboards/drag-drop-wizard/) for OpenSearch Dashboards. Experimental features allow you to test new functionality in OpenSearch. Because these features are still being developed, your testing and feedback can help shape the development of the feature before it's official released. We do not recommend use of experimental features in production. Additionally, this release adds maketime and makedate datetime functions for the SQL plugin. Creates a new [OpenSearch Playground](https://playground.opensearch.org) demo site for OpenSearch Dashboards. For a full list of release highlights, see the Release Notes. | 14 September 2022
 [2.2.1](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.2.1.md) | Includes gradle updates and bug fixes for gradle check. | 01 September 2022
-[2.2.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.2.0.md) | Includes support for Logistic Regression and RCFSummarize machine learning algorithms in ML Commons, Lucene or C-based Nmslib and Faiss libraries for approximate k-NN search, search by relevance using SQL and PPL queries, custom region maps for visualizations, and rollup enhancements. For a full list of release highlights, see the Release Notes. | 11 August 2022
+[2.2.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.2.0.md) | Includes support for Logistic Regression and RCF Summarize machine learning algorithms in ML Commons, Lucene or C-based Nmslib and Faiss libraries for approximate k-NN search, search by relevance using SQL and PPL queries, custom region maps for visualizations, and rollup enhancements. For a full list of release highlights, see the Release Notes. | 11 August 2022
 [2.1.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.1.0.md) | Includes support for dedicated ML node in the ML Commons plugin, relevance search and other features in SQL, multi-terms aggregation, and Snapshot Management. For a full list of release highlights, see the Release Notes. | 07 July 2022
 [2.0.1](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.0.1.md) | Includes bug fixes and maintenance updates for Alerting and Anomaly Detection. | 16 June 2022
 [2.0.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.0.0.md) | Includes document-level monitors for alerting, OpenSearch Notifications plugins, and Geo Map Tiles in OpenSearch Dashboards. Also adds support for Lucene 9 and bug fixes for all OpenSearch plugins. For a full list of release highlights, see the Release Notes. | 26 May 2022