# Machine learning for Amazon OpenSearch Service
<a name="ml"></a>

ML Commons is an OpenSearch plugin that provides 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. For more about the plugin, see [Machine learning](https://opensearch.org/docs/latest/ml-commons-plugin/index/) in the OpenSearch documentation. This chapter covers how to use the plugin with Amazon OpenSearch Service.

**Topics**
+ [Amazon OpenSearch Service ML connectors for AWS services](ml-amazon-connector.md)
+ [Amazon OpenSearch Service ML connectors for third-party platforms](ml-external-connector.md)
+ [Using CloudFormation to set up remote inference for semantic search](cfn-template.md)
+ [Unsupported ML Commons settings](#sm)
+ [OpenSearch Service flow framework templates](ml-workflow-framework.md)

## Unsupported ML Commons settings
<a name="sm"></a>

Amazon OpenSearch Service doesn't support use of the following ML Commons settings: 
+ `plugins.ml_commons.allow_registering_model_via_url`
+ `plugins.ml_commons.allow_registering_model_via_local_file`

**Important**  
On *production clusters*, do not disable the cluster setting `plugins.ml_commons.only_run_on_ml_node` (don't set it to `false`). The option to disable this safeguard is for facilitating development, but production clusters should be using the connectors. For more information, see [Amazon OpenSearch Service ML connectors for AWS services](ml-amazon-connector.md).

For more information on ML Commons settings, see [ML Commons cluster settings](https://opensearch.org/docs/latest/ml-commons-plugin/cluster-settings/).