The deploy model operation reads the model's chunks from the model index and then creates an instance of the model to cache into memory. This operation requires the `model_id`.
For information about user access for this API, see [Model access control considerations]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/model-apis/index/#model-access-control-considerations).
## Path and HTTP methods
```json
POST /_plugins/_ml/models/<model_id>/_deploy
```
#### Example request: Deploying to all available ML nodes
In this example request, OpenSearch deploys the model to any available OpenSearch ML node:
```json
POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
```
{% include copy-curl.html %}
#### Example request: Deploying to a specific node
If you want to reserve the memory of other ML nodes within your cluster, you can deploy your model to a specific node(s) by specifying the `node_ids` in the request body:
```json
POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
{
"node_ids": ["4PLK7KJWReyX0oWKnBA8nA"]
}
```
{% include copy-curl.html %}
#### Example response
```json
{
"task_id" : "hA8P44MBhyWuIwnfvTKP",
"status" : "DEPLOYING"
}
```
## Check the status of model deployment
To see the status of your model deployment and retrieve the model ID created for the new model version, pass the `task_id` as a path parameter to the Tasks API:
```json
GET /_plugins/_ml/tasks/hA8P44MBhyWuIwnfvTKP
```
{% include copy-curl.html %}
The response contains the model ID of the model version:
If a cluster or node is restarted, then you need to redeploy the model. To learn how to set up automatic redeployment, see [Enable auto redeploy]({{site.url}}{{site.baseurl}}/ml-commons-plugin/cluster-settings/#enable-auto-redeploy).
