using_pm_service.md

copyright	years 2016, 2017
lastupdated	2017-11-16

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Using the service

By using the modeling methods on the IBM® SPSS® Modeler modeling palette you can derive new information from your data and develop predictive models. Each method has certain strengths and is best suited for particular types of machine learning problems. {: .shortdesc}

For details about IBM® SPSS® Modeler and the modeling algorithms it provides, see IBM Knowledge Center.

After the input and output requirements of your {{site.data.keyword.Bluemix_notm}} application and IBM® SPSS® Modeler scoring branch design are implemented, your Data Analyst can change any internal aspect of the scoring branch. The Data Analyst can even change the model algorithm(s) used in a refresh operation, ensuring your ability to fine-tune your predictive analytics without needing to rewrite your applications.

Steps to bind the service with {{site.data.keyword.Bluemix_notm}} application

Complete the following steps to create your {{site.data.keyword.Bluemix_notm}} application and bind it to the {{site.data.keyword.pm_short}} service.

1. Download Node.js sample application code from github repository.

2. Use the cf create-service command to create a service instance:

   cf create-service pm-20 lite {local naming}
   

   {: codeblock}

   For example:

   cf create-service pm-20 lite my_pm_lite
   

   {: codeblock}

   This command creates one {{site.data.keyword.pm_short}} service instance with lite plan named my_pm_lite in your {{site.data.keyword.Bluemix_notm}} space.

3. Use the cf create-service-key command to create service credentials:

   cf create-service-key "{service instance name}" {vcap key name}
   

   {: codeblock}

   For example:

   cf create-service-key "IBM Watson Machine Learning - my instance" Credentials-1
   

   {: codeblock}

   This command creates {{site.data.keyword.pm_short}} service credentials.

4. Use the cf bind-service command to bind the service instance my_pm_lite to your application.

   cf bind-service AppName my_pm_service
   

   {: codeblock}

   For example:

   cf bind-service my_app1 my_pm_lite
   

   {: codeblock}

   This command binds the {{site.data.keyword.pm_short}} service instance my_pm_lite to the {{site.data.keyword.Bluemix_notm}} application my_app1.

5. {{site.data.keyword.pm_short}} credentials:

   After you bind the {{site.data.keyword.pm_short}} service instance to your {{site.data.keyword.Bluemix_notm}} application, the {{site.data.keyword.pm_short}} credentials are added to the VCAP_SERVICES environment variable:

    {   
        "pm-20": {      
            "name": "pm20-1",
            "label": "pm-20",
            "plan": "lite",
            "credentials": {
                "url": "https://ibm-watson-ml.mybluemix.net",
                "access_key": "XXXXXXXXXXXXX"
            }
        }       
    }

{: codeblock}

The VCAP_SERVICES environment variable includes the following information:

plan

The {{site.data.keyword.pm_short}} plan that is used in the service provisioning.

url

The address of the {{site.data.keyword.pm_short}} service instance.

access_key

The query parameter accessKey to pass in all requests to this service instance.

For example:

Get https://ibm-watson-ml.mybluemix.net/pm/v1/model/sales_model2?accesskey=XXXXXXXXXXXXX

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Example Node.js code that shows how to obtain the accessKey from the VCAP_SERVICES environment variable:

   if (process.env.VCAP_SERVICES) {
        var env = JSON.parse(process.env.VCAP_SERVICES);
        var credentials = env['pm-20'][0].credentials;
        var accessKey = credentials.access_key;
    }

{: codeblock}

Learn more

Ready to get started? To create an instance of a service or bind an application, see Using the service with Spark and Python models or Using the service with IBM® SPSS® models.

For more information about the API, see Service API for Spark and Python models or Service API for IBM® SPSS® models.

For more information about IBM® SPSS® Modeler and the modeling algorithms it provides, see IBM Knowledge Center.

For more information about IBM Data Science Experience and the modeling algorithms it provides, see https://datascience.ibm.com.