DISCLAIMER: This application is used for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review.

Create and deploy a scoring model to predict heart failure on IBM Cloud with the Watson Data Platform

In this Code Pattern, we will use a Jupyter Notebook on IBM Watson Studio to build a predictive model that demonstrates a potential health care use case. This a customized version of the Node.js sample app that is available with the Watson Machine Learning Service on IBM Cloud See the original app for a walkthrough of the source code.

When the reader has completed this Code Pattern, they will understand how to:

• Build a predictive model within a Jupyter Notebook
• Deploy the model to IBM Watson Machine Learning service
• Access the Machine Learning model via either APIs or a Nodejs app

Flow

1. The developer creates an IBM Watson Studio Workspace.
2. IBM Watson Studio depends on an Apache Spark service.
3. IBM Watson Studio uses Cloud Object storage to manage your data.
4. This lab is built around a Jupyter Notebook, this is where the developer will import data, train, and evaluate their model.
5. Import data on heart failure.
6. Trained models are deployed into production using IBM's Watson Machine Learning Service.
7. A Node.js web app is deployed on IBM Cloud calling the predictive model hosted in the Watson Machine Learning Service.
8. A user visits the web app, enters their information, and the predictive model returns a response.

Included components

• IBM Watson Studio: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.
• Jupyter Notebook: An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.
• PixieDust: Provides a Python helper library for IPython Notebook.

Featured technologies

• Artificial Intelligence: Artificial intelligence can be applied to disparate solution spaces to deliver disruptive technologies.
• Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.
• Node.js: An open-source JavaScript run-time environment for executing server-side JavaScript code.

Steps

1. Deploy the testing application
2. Create an instance of the Watson Machine Learning Service
3. Create a project in IBM Watson Studio and bind it to your Watson Machine Learning service instance
4. Save the credentials for your Watson Machine Learning Service
5. Create a notebook in IBM Watson Studio
6. Run the notebook in IBM Watson Studio
7. Deploy the saved predictive model as a scoring service using the web ui
8. Deploy the saved predictive model using APIs

Prerequisites

• An IBM Cloud Account

• An account on IBM Watson Studio.

• A space in IBM Cloud Dallas, London, Frankfurt, or Tokyo regions.

As of 12/14/2018, the Machine Learning service on IBM Cloud is only available in the Dallas, London, Frankfurt, or Tokyo regions.

1. Deploy the testing application

Use Ctrl-click on the Deploy to IBM Cloud button below to open the deployment process in a separate tab.

Note: Make sure to deploy the application to the same region and space as where the Apache Spark and Cloud Object Storage services were created when you signed up for IBM Watson Studio. Please take note of this space as later in this lab the Watson Machine Learning service needs to be deployed into the same space.

• Click on Deploy to deploy the application.

• A Toolchain and Delivery Pipeline will be created for you to pull the app out of Github and deploy it in to IBM Cloud. Click on the Delivery Pipeline tile to see the status of the deployment. Wait for the Deploy Stage to complete successfully.

2. Create an instance of the Watson Machine Learning Service

• In your browser go to the IBM Cloud Dashboard and click Catalog.

• Search for Machine Learning, Verify this service is being created in the same space as the app in Step 1, and click Create.

  

• On the Watson ML Dashboard select Connections on left menu panel, and Create Connection. Select the application that you deployed earlier in Step 1 of this lab connecting this Watson ML service to the Cloud Foundry application deployed.

  

• Click Connect and restage app when you’re prompted to restage your application. The app will take a couple of minutes to be back in the running state.

3. Create a project in IBM Watson Studio and bind it to your Watson Machine Learning service instance

• Sign up for IBM's Watson Studio.
• Create a new project by clicking + New project and choosing Data Science:

Note: By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Take note of your service names as you will need to select them in the following steps.

Note: When creating your Object Storage service, select the Free storage type in order to avoid having to pay an upgrade fee.

Note: Services created must be in the same region, and space, as your Watson Studio service.

• Enter a name for the project name and click Create.

• From within the new project Overview panel, click Add to project on the top right, selecting Data asset.

  

  A panel on the right of the screen appears, select load and click on Browse to upload the data file you'll use to create a predictive model.

• On your machine, browse to the location of the file patientdataV6.csv in this repository in the data/ directory. Select the file and click on Open (or the equivalent action for your operating system).

• Once successfully uploaded, the file should appear in the Data Assets section.

  

• Click on the Services pull-down at the top of the page, click on Watson services and click `+ Add service':

  

• Choose your existing Machine Learning instance and click on Select.

  

• The Watson Machine Learning service is now listed as one of your Associated Services.

• Leave the browser tab open for later.

4. Save the credentials for your Watson Machine Learning Service

• In a different browser tab go to https://cloud.ibm.com and log in to the Dashboard.

• Click on your Watson Machine Learning instance under Services, click on Service credentials and then on View credentials to see the credentials.

  

• Save the username, password and instance_id to a text file on your machine. You’ll need this information later in your Jupyter notebook.

5. Create a notebook in IBM Watson Studio

• In Watson Studio using the project you've created, click on + Add to project -> Notebook OR in the Assets tab under Notebooks choose + New notebook to create a notebook.

• Select the From URL tab.

• Enter a name for the notebook.

• Optionally, enter a description for the notebook.

• Under Notebook URL provide the following url: https://github.com/dzwietering/watson-dojo-pm-tester/blob/master/notebooks/ML%20Predictive%20Model.ipynb

• Select the Spark runtime with Python 3.5 .

• Click the Create button.

  

6. Run the notebook in IBM Watson Studio

• Place your cursor in the first code block in the notebook.

  

• Click on the Find and Add data icon -- see step 1 in diagram below -- and then select Insert to code under the file patientdataV6.csv. This is step 2 in diagram below. Finally select Insert SparkSession Data Frame -- which is step 3 in diagram below.

  

Note: Make sure to rename the variable to df_data and add .option('inferSchema','True')\.

• Goto the cell that says Stop here !!!! during Step 5, insert the username, password, and instance_id that you saved from your Watson Machine Learning instance into the code before running it. Do the same after Step 6 Save model to WML Service.

  

• Click on the Run icon to run the code in the cell.

  

• Move your cursor to each code cell and run the code in it. Read the comments for each cell to understand what the code is doing. Important when the code in a cell is still running, the label to the left changes to In [*]:. Do not continue to the next cell until the code is finished running.

7. Deploy the saved predictive model as a scoring service using the web UI

• In Watson Studio go to you project, under Assets -> Models and click on the model you've created: Heart Failure Prediction Model.

• Go to the Deployments tab and + Add Deployment.

• Give your Deployment a name, click Create, and it should show up with STATUS of DEPLOY_SUCCESS.

• Restart the Node.js Web App. For this, return to your IBM Cloud Dashboard, choose your application, and select restart from the More action three vertical dots

8. Deploy the saved predictive model using APIs

• To deploy the model using the APIs instead of using the Web UI, at Step 6.1, add the instance_id from yout Watson Machine Learning Service credentials. During Step 6.2, after running the second cell, get the model_id and put it in the cell that follows. Put the deployment_id in the cell under Montitor the status of deployment. For Step 6.3, add the scoring_url to the cell.

Sample Output

• In the dashboard, Click on the application name, then choose Visit App URL from the Overview page to open the application in a separate tab.

• When the application appears click on Score now to test the scoring model with the default values.

• Verify that the model predicts that there is a risk of heart failure for the patient with these medical characteristics.

• Click Close. Run the app again with the following parameters.

• Verify that the model predicts that there is not a risk of heart failure for the patient with these medical characteristics.

Learn more

• Artificial Intelligence Code Patterns: Enjoyed this Code Pattern? Check out our other AI Code Patterns.
• Data Analytics Code Patterns: Enjoyed this Code Pattern? Check out our other Data Analytics Code Patterns
• AI and Data Code Pattern Playlist: Bookmark our playlist with all of our Code Pattern videos
• With Watson: Want to take your Watson app to the next level? Looking to utilize Watson Brand assets? Join the With Watson program to leverage exclusive brand, marketing, and tech resources to amplify and accelerate your Watson embedded commercial solution.
• Watson Studio: Master the art of data science with IBM's Watson Studio
• Spark on IBM Cloud: Need a Spark cluster? Create up to 30 Spark executors on IBM Cloud with our Spark service

License

This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.

Apache Software License (ASL) FAQ