lab_3.md

Introduction to Predicive Modeling with Watson Machine Learning

IBM Watson® Machine Learning helps data scientists and developers accelerate AI and machine-learning deployment. With its open, extensible model operation, Watson Machine Learning helps businesses simplify and harness AI at scale across any cloud.

In this lab we will be deploying a model to predict heart failure with Watson Machine Learning

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

This code pattern can be thought of as two distinct parts:

1. A predictive model will be built using Spark within a Jupyter Notebook on IBM Watson Studio. The model is then deployed to the Watson Machine Learning service, where it can be accessed via a REST API.

2. A Node.js web app that allows a user to input some data to be scored against the previous model.

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

• Build a predictive model within a Jupyter Notebook on Watson Studio
• Deploy the model to the IBM Watson Machine Learning service
• Via a Node.js app, score some data against the model via an API call to the Watson Machine Learning service

Workshop

Prerequisites

• An IBM Cloud Account
• An account on IBM Watson Studio.

Steps

Step 1 : Setup project and data in Watson Studio

To complete this code pattern we'll need to do a few setup steps before creating our model. In Watson Studio we need to: create a project, add our patient data (which our model will be based on), upload our notebook, and provision a Watson Machine Learning service.

1.1. Create a project in Watson Studio

• Log into IBM's Watson Studio. Once in, you'll land on the dashboard.

• Create a new project by clicking + New project and choosing Data Science:

  

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

NOTE: By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Select the Free storage type to avoid fees.

1.2 Add patient data as an asset

The data used in this example was generated using a normal distribution. Attributes such as age, gender, heartrate, minutes of exercise per week, and cholesterol are used to create the model we will eventually deploy.

• From the new project Overview panel, click + Add to project on the top right and choose the Data asset type.

  

• A panel on the right of the screen will appear to assit you in uploading data. Follow the numbered steps in the image below.

  • Ensure you're on the Load tab. [1]
  • Click on the browse option. From your machine, browse to the location of the patientdataV6.csv file in this repository, and upload it. [not numbered]
  • Once uploaded, go to the Files tab. [2]
  • Ensure the patientdataV6.csv appears. [3]

  

• TIP: Once successfully uploaded, the file should appear in the Data assets section of the Assets tab.

1.3 Provision a Watson Machine Learning service

• Click on the navigation menu on the left (☰) to show additional options. Click on the Watson Services option.

  

• From the overview page, click + Add service on the top right and choose the Machine Learning service. Select the Lite plan to avoid fees.

• Once provisioned, you should see the service listed in the Watson Services overview page. Select the service by opening the link in a new tab. We're now in the IBM Cloud tool, where we will create service credentials for our now Watson Machine Learning service. Follow the numbered steps in the image below. We'll be using these credentials in Step 2, so keep them handy!.

  

• TIP: You can now go back the project via the navigation menu on the left (☰).

  

1.4 Create a notebook in Watson Studio

The notebook we'll be using can be viewed in notebooks/predictiveModel.ipynb, and a completed version can be found in examples/exampleOutput.ipynb.

• From the new project Overview panel, click + Add to project on the top right and choose the Notebook asset type. Fill in the following information:

  • Select the From URL tab. =

  • Enter a Name for the notebook and optionally a description.

  • Under Notebook URL provide the following url: https://github.com/pmmistry/intro-to-jupyter-notebooks/blob/master/notebooks/intro-to-predictive-modeling.ipynb

  • Important For Runtime select the Default Spark 2.3 & Python 3.6 option. [4]

  

• TIP: Once successfully imported, the notebook should appear in the Notebooks section of the Assets tab.

  

Step 2 : Setup project and data in Watson Studio

Now that we're in our Notebook editor, we can start to create our predictive model by stepping through the notebook.

2.1 Start stepping through the notebook

• Click the (►) Run button to start stepping through the notebook.

• When you reach the cell entitled 2. Load and explore data pause and follow the instructions in that cell. On the very next cell we need to add our data. Follow the numbered steps in the image below.

  

  • Click on the Data icon. [1]
  • Select the Insert to code option under the file patientdataV6.csv. [2]
  • Choose the Insert SparkSession Data Frame option. [3]

  

• The above step will have inserted a chunk of code into your notebook. We need to make two changes:

  • Rename the df_data_1 variable to df_data. [1]
  • Re-add the line .option('inferSchema','True')\ to the spark.read() call. [2]

  

• Keep stepping through the code, pausing on each step to read the code and see the output for the opertion we're performing. At the end of Step 4 we'll have used the Random Forest Classifier from PySpark to create a model LOCALLY.

  

2.2 Save the model

The gist of the next two steps is to use the Watson Machine Learning Python client to persist and deploy the model we just created.

• At the beginning of Step 5. Persist model, before we deploy our model, we need up update the cell with credentials from our Watson Machine Learning service. (Remember that from Step 1.3 Provision a Watson Machine Learning service?)

• Update the wml_credentials variable below. Copy and paste the entire credential dictionary, which can be found on the Service Credentials tab of the Watson Machine Learning service instance created on the IBM Cloud.

  

• Keep stepping through the code, pausing on each step to read the code and see the output for the opertion we're performing. At the end of Step 5 we'll have used the Watson Machine Learning service to persist our predictive model! 🎉

  

2.3 Deploy the model

• Now let's run Step 6 of the notebook. Deploy our model so we can have an endpoint to score data against.

  

Now that we have an API, let's create a client side interface that a typical user would interact with.

Step 3 : Working With Deployed Model

Once the model is deployed you may be wondering how you can test and work with the model .

3.1 Go to Deployed Model

Go back to your assets tab and you should see your model Heart Failure Prediction Model under Model asset

3.2 Deployment Overview

Once you click on your model you should see the overview of your model

Click on the Deployments tab and click on your model . You should now see your model deployed successfully. Click on your model and you should see how you can implement your model

3.3 Implementing and Testing your model

Once you click your model from the Deployments tab you should see Overview , Implementation and Test

Click on Implementation tab to see how you can implement your model using cUrl, Java, Javascript , Python and Scala

Click on the Test tab to test your model by filling in the input schema

Congrats! You just learned how to deploy your model to Watson Machine Learning 🎉🎉🎉

If you are interested in building a Node.js App with this model check out this tutorial

References

• Create and deploy a scoring model to predict heartrate failure