diff --git a/scoring_file_v_2_0_0.py b/scoring_file_v_2_0_0.py
deleted file mode 100644
index 758ff3d..0000000
--- a/scoring_file_v_2_0_0.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# ---------------------------------------------------------
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# ---------------------------------------------------------
-import json
-import logging
-import os
-import pickle
-import numpy as np
-import pandas as pd
-import joblib
-
-import azureml.automl.core
-from azureml.automl.core.shared import logging_utilities, log_server
-from azureml.telemetry import INSTRUMENTATION_KEY
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-from inference_schema.parameter_types.pandas_parameter_type import PandasParameterType
-from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType
-
-data_sample = PandasParameterType(pd.DataFrame({"area_code": pd.Series(["example_value"], dtype="object"), "state_code": pd.Series(["example_value"], dtype="object"), "data_type_code": pd.Series(["example_value"], dtype="object"), "industry_code": pd.Series(["example_value"], dtype="object"), "supersector_code": pd.Series(["example_value"], dtype="object"), "series_id": pd.Series(["example_value"], dtype="object"), "year": pd.Series([0], dtype="int16"), "period": pd.Series(["example_value"], dtype="object"), "footnote_codes": pd.Series(["example_value"], dtype="object"), "seasonal": pd.Series(["example_value"], dtype="object"), "supersector_name": pd.Series(["example_value"], dtype="object"), "industry_name": pd.Series(["example_value"], dtype="object"), "data_type_text": pd.Series(["example_value"], dtype="object"), "state_name": pd.Series(["example_value"], dtype="object"), "area_name": pd.Series(["example_value"], dtype="object")}))
-input_sample = StandardPythonParameterType({'data': data_sample})
-
-result_sample = NumpyParameterType(np.array([0.0]))
-output_sample = StandardPythonParameterType({'Results':result_sample})
-sample_global_parameters = StandardPythonParameterType(1.0)
-
-try:
-    log_server.enable_telemetry(INSTRUMENTATION_KEY)
-    log_server.set_verbosity('INFO')
-    logger = logging.getLogger('azureml.automl.core.scoring_script_v2')
-except:
-    pass
-
-
-def init():
-    global model
-    # This name is model.id of model that we want to deploy deserialize the model file back
-    # into a sklearn model
-    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')
-    path = os.path.normpath(model_path)
-    path_split = path.split(os.sep)
-    log_server.update_custom_dimensions({'model_name': path_split[-3], 'model_version': path_split[-2]})
-    try:
-        logger.info("Loading model from path.")
-        model = joblib.load(model_path)
-        logger.info("Loading successful.")
-    except Exception as e:
-        logging_utilities.log_traceback(e, logger)
-        raise
-
-@input_schema('Inputs', input_sample)
-@input_schema('GlobalParameters', sample_global_parameters, convert_to_provided_type=False)
-@output_schema(output_sample)
-def run(Inputs, GlobalParameters=1.0):
-    data = Inputs['data']
-    result = model.predict(data)
-    return {'Results':result.tolist()}