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Build, Deploy, and Run a Machine Learning Model using Watson Studio Notebook

Tutorial Scenario

Golden Bank is a leading mortgage provider through their network of neighborhood branches. This tutorial cover these goals:

  • The bank uses AI to process loan applications and needs to avoid unanticipated risk and ensure that its applicants are being treated fairly.
  • Based on a new regulation, the bank cannot lend to underqualified loan applicants. The bank has existing data for loan applications in a Db2 Warehouse. The bank needs to use the data to train AI models without moving it.
  • The bank wants to run a campaign to offer lower mortgage rates. The bank needs a consolidated view of applicants to identify the highest value customers to target and help to determine the best rates to offer them.

Table of Contents

Prerequisite: Prepare Services

Before we begin, we need to make sure we have the required services for the exercise. If you followed the sign-up process all required services should have been provisioned. Follow the steps below to verify or create missing services.

  1. Log into your IBM Cloud account.
  2. From the navigation menu in the top left corner, scroll down and choose Cloud Pak for Data > Services.
  3. Click Launch Cloud Pak for Data.
  4. From the Cloud Pak for Data navigation menu in the top left corner, choose Services > Service instances.
  5. Use the Product drop-down list to determine whether there is an existing Watson Studio service instance.
  6. If you need to create a Watson Studio service instance, click Add service.
  7. Select Watson Studio.
    1. Select Dallas as the region.
    2. Select the Lite plan.
    3. Select the Default resource group.
    4. Click Create.
  8. Repeat these steps to verify or provision the following additional services.
  9. Verify your screen looks like the following image.

Exercise 1: Create Project

Create the sample project for the exercise:

  1. Login to IBM Cloud Pak for Data
    • Select "Dallas" under "Work with resources in this IBM Cloud region"
  2. From the navigation menu in the top left corner select Gallery.
  3. Type "MLOps" in the search box and click on the MLOps and Trustworthy AI guided tutorial sample project.
  4. Click Create project.
  5. Take the default name and select your Cloud Object Storage instance from the list.
  6. Click Create and then View new project
  7. Close the popup window and select the Assets tab to verify your project looks like the following image:

Exercise 2: Set up Tracking

  1. Set up tracking for your model in Watson Knowledge Catalog:
    1. From the IBM Cloud Pak for Data navigation menu, choose Catalogs > View all catalogs.
    2. Click Create Catalog.
    3. Enter the catalog name as “Mortgage Approval Catalog” with no leading or trailing spaces.
    4. Select your IBM Cloud Object Storage from the list for the catalog assets.
    5. Select Enforce data policies, confirm the selection, and accept the defaults for the other fields.
    6. Click Create.
  2. Create model entry in the model inventory:
    1. From the IBM Cloud Pak for Data navigation menu, choose Catalogs > Model Inventory.
    2. Click New model entry.
    3. Enter the model entry name as “Mortgage Approval Model Entry” with no leading or trailing spaces.
    4. Click Save.
    5. Verify your model entry looks like the following image.

Exercise 3: Build and Save a Model

We will run the first notebook in the sample project in order to ...:

  • Set up AI Factsheets used to track the lifecycle of the mode.
  • Load the training data.
  • Specify the target, categorical, and numerical columns along with the thresholds used to build the model.
  • Build data pipelines.
  • Build machine learning models.
  • View the model results.
  • Save the model.
  1. From the IBM Cloud Pak for Data navigation menu, choose Projects > View all projects.
  2. Open the MLOps and Trustworthy AI project.
  3. On the Assets tab, click Source Code > Notebooks.
  4. Open the 1-model-training-with-factsheets notebook.
  5. Since the notebook is in read-only mode, click the Edit (pencil) icon to place the notebook in edit mode.
  6. Under the Provide your IBM Cloud API key section, you need to pass your credentials to the Machine Learning API service.
    • If you don't already have a saved API key, follow the instructions in notebook to create an API key.
  7. Update the cell ibmcloud_api_key = '' with your API key.
  8. To run all the cells in the notebook, click Cell > Run All. Alternatively, you can run the notebook cell by cell if you want to explore each cell and its output.
  9. Take a few minutes to review the steps to build, train, verify, and save this random forest model.
  10. After the last notebook cell is finished and the model is stored, click File > Stop Kernel and confirm to shut down the kernel.

You just built and saved a model “Mortgage Approval Prediction Model” to your project.

Exercise 4: View Model’s AI Factsheet

View the model’s AI Factsheet:

  1. From the Cloud Pak for Data navigation menu, choose Projects > View all projects.
  2. Open the MLOps and Trustworthy AI project.
  3. On the Assets tab, click Models > Models.
  4. Open Mortgage Approval Prediction Model that you previously created.
  5. Review the AI Factsheet for your model.
  6. Scroll to the Training metrics and Training tags sections to review the captured training metadata.
  7. Verify your screen looks like the following image.

Exercise 5: Associate the Model with a Model Entry

We need to associate the model with a model entry.

  1. Scroll up on the model page, and click Track this model.
    1. In the pop-up dialog box, choose Select an existing model entry.
    2. From the list of model entries, select Mortgage Approval Model Entry.
    3. Click Track to close the dialog box
  2. Back on the model page, click Open in model inventory.
  3. On the Model entry page, click the Asset tab.
  4. Under Model tracking, you can see that AI Factsheets track models through their lifecycle. This model is still in the Develop stage as it has not been deployed yet.
  5. Verify your screen looks like the following image.

Exercise 6: Promote Model to Deployment Space

Before you can deploy the model, you need to promote the model to a new deployment space.

  1. From the IBM Cloud Pak for Data navigation menu, choose Catalogs > Model inventory.
  2. For the Mortgage Approval Model Entry, click View details.
  3. Click Asset tab.
  4. From the model entry, under the Develop phase, click Mortgage Approval Prediction Model.
  5. Click Open in project to open the model in the MLOps and Trustworthy AI project.
  6. On the model page, click Promote to deployment space.
  7. For the Target space, select Create a new deployment space.
  8. For the deployment space name, enter “Golden Bank Preproduction Space” with no leading or trailing spaces.
  9. Select your storage service from the list.
  10. Select your provisioned machine learning service from the list.
  11. Click Create and after the deployment space is ready click Close.
  12. For the Target space, ensure that Golden Bank Preproduction Space is selected.
  13. Check the Go to model in the space after promoting it option.
  14. Click Promote.

Exercise 7: Deploy the Model

Create an online deployment for the model.

  1. On the deployment space screen, click New deployment.
  2. For the Deployment type, select Online.
  3. For the Name, enter "Mortgage Approval Model Deployment" with no leading or trailing spaces.
  4. For the Serving Name, enter “mortgage_approval_service_<your_unique_id>”, append some characters to make it unique if name is taken.
  5. Click Create.
  6. Navigate to Catalogs > Model inventory.
  7. For the Mortgage Approval Model Entry, click View details.
  8. Click the Asset tab. Under Model tracking, you can see that the model is now in the Deploy stage.
  9. Verify your model entry looks like the following image.

Exercise 8: Run the model

Make predictions using the deployed model.

  1. Add the test dataset to the deployment space
    1. Download the test dataset GoldenBank_TestData.csv from this repo.
      1. Click "Raw" in the top right corner above the CSV content view
      2. From your browser "File" menu, click "Save page as" to store a local copy of the CSV file
    2. From the Cloud Pak for Data navigation menu, click Deployments
    3. Select the Spaces tab and click on "Golden Bank Preproduction Space" from the list of Deployments
    4. Click on "Drop files here or browse for files to upload" to upload the test dataset.
    5. Verify that the test dataset CSV file appears under Data assets on the Assets tab.
  2. Open model deployment in the deployment space.
    1. Click Deployments tab.
    2. Click Mortgage Approval Model Deployment.
  3. Make a prediction request to the model - using a dataset CSV file
    1. On the Test tab, click Search in space.
    2. Click Data asset.
    3. Click GoldenBank_TestData.csv and Confirm.
    4. Click Predict to make prediction requests for 168 entries in the test dataset.
    5. Verify your screen looks like the following image.
    6. Close the Prediction results dialog.
  4. Make a prediction request to the model – using a local JSON file
    1. Download the test json file GoldenBank_TestData.json from this repo.
      1. Click "Raw" in the top right corner above the JSON content view
      2. From your browser "File" menu, click "Save page as" to store a local copy of the JSON file
    2. Under "Enter input data", click Paste JSON.
    3. Click Browse local files and select the "GoldenBank_TestData.json" downloaded in the previous step.
    4. Click Predict to make a prediction request for the entry in json.
    5. You should see the Binary classification result for 1 Record, i.e. a prediction of 0 and 94% confidence.
    6. Close the result dialog
    7. Repeat the steps to "Browse local files", scroll down the json editor and change the following values:
      • 3rd value from 10 to 30 (YRS_WITH_CURRENT_EMPLOYER)
      • 5th value from 2000 to 200 (CREDITCARD_DEBT)
    8. Verify your screen looks like in this screenshot
    9. Click Predict to make a prediction request for the entry in json.
    10. Now you should see a prediction of 1 with a high confidence around 80%.