要将模型注册表与处理作业创建的模型集成，您可以按照以下步骤进行操作：

1. 创建模型并将其注册到模型注册表中：

import boto3

model_name = 'my_model'
model_package_arn = 'arn:aws:sagemaker:us-west-2:123456789012:model-package/my-model-package'

sagemaker_client = boto3.client('sagemaker')

response = sagemaker_client.create_model(
    ModelName=model_name,
    ExecutionRoleArn='arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-XXXXXX',
    PrimaryContainer={
        'ModelPackageName': model_package_arn,
        'Environment': {
            'Key': 'value'
        }
    }
)

model_arn = response['ModelArn']

2. 创建处理作业并指定要使用的模型：

job_name = 'my_job'
input_data = 's3://my-bucket/input_data'
output_data = 's3://my-bucket/output_data'
processing_code = 's3://my-bucket/processing_code.py'

processing_inputs = [
    {
        'InputName': 'input_data',
        'S3Input': {
            'S3Uri': input_data,
            'LocalPath': '/opt/ml/processing/input_data',
            'S3DataType': 'S3Prefix',
            'S3InputMode': 'File',
            'S3DataDistributionType': 'FullyReplicated'
        }
    }
]

processing_outputs = [
    {
        'OutputName': 'output_data',
        'S3Output': {
            'S3Uri': output_data,
            'LocalPath': '/opt/ml/processing/output_data',
            'S3UploadMode': 'EndOfJob'
        }
    }
]

processing_job = {
    'ProcessingInputs': processing_inputs,
    'ProcessingOutputConfig': {
        'Outputs': processing_outputs
    },
    'ProcessingJobName': job_name,
    'ProcessingResources': {
        'ClusterConfig': {
            'InstanceCount': 1,
            'InstanceType': 'ml.m5.large',
            'VolumeSizeInGB': 30
        }
    },
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 1800
    },
    'AppSpecification': {
        'ImageUri': '763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.6.0-cpu-py36-ubuntu16.04'
    },
    'RoleArn': 'arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-XXXXXX',
    'Environment': {
        'Key': 'value'
    },
    'ModelName': model_name
}

response = sagemaker_client.create_processing_job(**processing_job)

以上是一个简单的示例，其中使用了Python SDK（boto3）来调用Amazon SageMaker API。您需要根据自己的实际情况调整代码中的参数和配置。