1

Suppose that I already have a scikit-learn model and I want to save this to my Watson Machine Learning and deploy it using the python client.

The python client docs: http://wml-api-pyclient.mybluemix.net

I have like: 

clf = svm.SVC(kernel='rbf')
clf.fit(train_data, train_labels)

# Evaluate your model.
predicted = clf.predict(test_data)

What I want to do is to deploy this model as a web service accessible via REST API.

I read in the Watson Machine Learning Documentation here: https://dataplatform.cloud.ibm.com/docs/content/analyze-data/wml-ai.html?audience=wdp&context=analytics

but I'm having trouble when deploying the model.

elliptic_kid
  • 62
  • 6

2 Answers2

1

With scikit learn model, Watson Machine Learning expects a pipeline object instead of just a fit model object. This is so that you also deploy the data transformation and preprocessing logic to the same endpoint. For example, try changing your code to: 

scaler = preprocessing.StandardScaler()
clf = svm.SVC(kernel='rbf')
pipeline = Pipeline([('scaler', scaler), ('svc', clf)])
model = pipeline.fit(train_data, train_labels)

Then you will be able to deploy the model by following the docs here:http://wml-api-pyclient.mybluemix.net/#deployments

From your Notebook in Watson Studio, you can just 

from watson_machine_learning_client import WatsonMachineLearningAPIClient

wml_credentials = {
                   "url": "https://ibm-watson-ml.mybluemix.net",
                   "username": "*****",
                   "password": "*****",
                   "instance_id": "*****"
                  }

client = WatsonMachineLearningAPIClient(wml_credentials)

and then use the client to deploy the model after saving the model first to the repository.

You can see how to accomplish all of this in this tutorial notebook: https://dataplatform.cloud.ibm.com/exchange/public/entry/view/168e65a9e8d2e6174a4e2e2765aa4df1 from the Community

Keyur Potdar
  • 7,158
  • 6
  • 25
  • 40
elliptic_kid
  • 62
  • 6
1

You can also deploy it as a python function. what you need is to wrap all your functionalities into a single deployable function (learn python closure).

The way you use the credential is the same in this Method.

  • Step 1 : Define the function
  • Step 2 : Store the function in the repository

after that, you can deploy it and access by two ways

  1. using the Python client
  2. using the REST API

This has been explained in detail in this see this post