def METHOD_NAME():
    service_name = 'isvc-pmml'
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        pmml=V1beta1PMMLSpec(
            storage_uri='gs://kfserving-examples/models/pmml',
            resources=V1ResourceRequirements(
                requests={'cpu': '10m', 'memory': '128Mi'},
                limits={'cpu': '100m', 'memory': '256Mi'}
            )
        )
    )
    isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                   kind=constants.KSERVE_KIND,
                                   metadata=client.V1ObjectMeta(
                                        name=service_name, namespace=KSERVE_TEST_NAMESPACE),
                                   spec=V1beta1InferenceServiceSpec(predictor=predictor))
    kserve_client = KServeClient(config_file=os.environ.get("KUBECONFIG", "~/.kube/config"))
    kserve_client.create(isvc)
    kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE)
    res = predict(service_name, './data/pmml_input.json')
    assert (res["predictions"] == [{'Species': 'setosa',
                                    'Probability_setosa': 1.0,
                                    'Probability_versicolor': 0.0,
                                    'Probability_virginica': 0.0,
                                    'Node_Id': '2'}])
    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)