Task 1: Defining and deploying a machine learning model in Watson Machine Learning

Step 1: Defining and deploying a machine learning model

1. Use your credentials to log into Watson Studio and create a new project.
2. Choose one of the following procedures to create and deploy your machine learning model:

   Option	Procedure
   Using a PMML file	Create a new asset by importing the Standard scaler PMML file. Deploy the model in Watson Machine Learning by following the instructions in the Deployment Spaces documentation .
   Using a Jupyter Notebook	Create a new asset using the Build a Loan default PMML scoring model with scikit-learn in Watson ML Jupyter Notebook. Update the following values in the Notebook: url: your Watson Machine Learning API endpoint. apikey: see instructions below for obtaining it. space_id: your deployment space ID. Run the Notebook to build and deploy the machine learning model.

Step 2: Gathering connection details

Retrieve the following information from Watson Studio to configure the machine learning provider in your decision service:

• The space ID, which can be found in the deployment space settings.
• The model deployment URL. For example: https://<location>.ml.cloud.ibm.com/ml/v4
• The authentication URL. For example: https://iam.bluemix.net/identity/token
• An API Key, which can be obtained from your IBM Cloud account.