# Deploy a custom model


You can deploy a custom model with the Amazon Bedrock console, AWS Command Line Interface, or AWS SDKs. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html). 

**Topics**
+ [

## Deploy a custom model (console)
](#deploy-custom-model-console)
+ [

## Deploy a custom model (AWS Command Line Interface)
](#deploy-custom-model-cli)
+ [

## Deploy a custom model (AWS SDKs)
](#deploy-custom-model-sdk)

## Deploy a custom model (console)


You deploy a custom model from the **Custom models** page as follows. You can also deploy a model from the **Custom model on-demand** page with the same fields. To find this page, in **Inference and Assessment** in the navigation pane, choose **Custom model on-demand**.

**To deploy a custom model**

1. Sign in to the AWS Management Console using an [IAM role with Amazon Bedrock permissions](https://docs.aws.amazon.com//bedrock/latest/userguide/getting-started.html), and open the Amazon Bedrock console at [https://console.aws.amazon.com/nova/](https://console.aws.amazon.com/nova/).

1. From the left navigation pane, choose **Custom models** under **Foundation models**.

1. In the **Models** tab, choose the radio button for the model you want to deploy.

1. Choose **Set up inference** and choose **Deploy for on-demand**.

1. In **Deployment details**, provide the following information:
   + **Deployment Name** (required) – Enter a unique name for your deployment.
   + **Description** (optional) – Enter a description for your deployment.
   + **Tags** (optional) – Add tags for cost allocation and resource management.

1. Choose **Create**. When the status shows `Completed`, your custom model is ready for on-demand inference. For more information about using the custom model, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html).

## Deploy a custom model (AWS Command Line Interface)


To deploy a custom model for on-demand inference using the AWS Command Line Interface, use the `create-custom-model-deployment` command with your custom model's Amazon Resource Name (ARN). This command uses the [CreateCustomModelDeployment](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModelDeployment.html) API operation. It returns the deployment's ARN that you can use as the `modelId` when making inference requests. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html).

```
aws bedrock create-custom-model-deployment \
--model-deployment-name "Unique name" \
--model-arn "Custom Model ARN" \
--description "Deployment description" \
--tags '[
    {
        "key": "Environment",
        "value": "Production"
    },
    {
        "key": "Team",
        "value": "ML-Engineering"
    },
    {
        "key": "Project",
        "value": "CustomerSupport"
    }
]' \
--client-request-token "unique-deployment-token" \
--region region
```

## Deploy a custom model (AWS SDKs)


To deploy a custom model for on-demand inference, use the [CreateCustomModelDeployment](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModelDeployment.html) API operation with your custom model's Amazon Resource Name (ARN). The response returns the deployment's ARN that you can use as the `modelId` when making inference requests. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html).

The following code shows how to use the SDK for Python (Boto3) to deploy a custom model. 

```
def create_custom_model_deployment(bedrock_client):
    """Create a custom model deployment
    Args:
        bedrock_client: A boto3 Bedrock client for making API calls
 
    Returns:
        str: The ARN of the created custom model deployment
 
    Raises:
        Exception: If there is an error creating the deployment
    """
 
    try:
        response = bedrock_client.create_custom_model_deployment(
            modelDeploymentName="Unique deployment name",
            modelArn="Custom Model ARN",
            description="Deployment description",
            tags=[
                {'key': 'Environment', 'value': 'Production'},
                {'key': 'Team', 'value': 'ML-Engineering'},
                {'key': 'Project', 'value': 'CustomerSupport'}
            ],
            clientRequestToken=f"deployment-{uuid.uuid4()}"
        )
 
        deployment_arn = response['customModelDeploymentArn']
        print(f"Deployment created: {deployment_arn}")
        return deployment_arn
 
    except Exception as e:
        print(f"Error creating deployment: {str(e)}")
        raise
```