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import mlflow.pyfunc
model_name = "NewYorkTaxiPredictModel"
model_production_uri = "models:/{model_name}/production".format(model_name=model_name)
print("Loading Production model from URI: '{model_uri}'".format(model_uri=model_production_uri))
production_model = mlflow.pyfunc.load_model(model_production_uri)
# load test data
test_pd_df = NYorkTaxiFairPrediction.read_parquet_folder_as_pandas('/dbfs/mnt/blogs_pl/taxi_fare_feature_eng_test_sample1')
# make predictions
predictions = production_model.predict(test_pd_df)
display(predictions)
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