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I've been working recently on deploying a machine learning model as a web service. I used Azure Machine Learning Studio for creating my own Workspace ID and Authorization Token. Then, I trained LogisticRegressionCV model from sklearn.linear_model locally on my machine (using python 2.7.13) and with the usage of below code snippet I wanted to publish my model as web service:

from azureml import services

@services.publish('workspaceID','authorization_token')

@services.types(var_1= float, var_2= float)

@services.returns(int)

def predicting(var_1, var_2):

    input = np.array([var_1, var_2].reshape(1,-1)

return model.predict_proba(input)[0][1]

where the input variable is a list with data to be scored and the model variable contains trained classifier. Then after defining the above function, I want to make a prediction on the sample input vector:

predicting.service(1.21, 1.34)

However, the following error occurs:

RuntimeError: Error 0085: The following error occurred during script 

evaluation, please view the output log for more information:

And the most important message in log is:

AttributeError: 'module' object has no attribute 'LogisticRegressionCV'

The error is strange to me because when I was using normal sklearn.linear_model.LogisticRegression everything was fine. I was able to make predictions sending POST requests to created endpoint, so I guess sklearn worked correctly. After changing to LogisticRegressionCV it does not.

Therefore I wanted to update the sklearn on my workspace.

Do you have any ideas on how to do it? Or even more general question: how to install any python module on azure machine learning studio in a way to use predict functions of any model I developed locally?

1 Answer

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by (9.6k points)

1. Via virtualenv, create a python project and active it.

2. Install all the packages necessary using pip

3. Package all files under the path Lib\site-packages as a zip file

4. Upload the zip file on your AML Workspace as dataset

5. Follow this document for further process

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