Files
MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-with-controlled-rollout/score.py
2019-11-27 21:02:21 +00:00

29 lines
917 B
Python

import pickle
import json
import numpy
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from azureml.core.model import Model
def init():
global model
# note here "sklearn_regression_model.pkl" is the name of the model registered under
# this is a different behavior than before when the code is run locally, even though the code is the same.
model_path = Model.get_model_path('sklearn_regression_model.pkl')
# deserialize the model file back into a sklearn model
model = joblib.load(model_path)
# note you can pass in multiple rows for scoring
def run(raw_data):
try:
data = json.loads(raw_data)['data']
data = numpy.array(data)
result = model.predict(data)
# you can return any data type as long as it is JSON-serializable
return result.tolist()
except Exception as e:
error = str(e)
return error