Run the second notebook included in the sample project to:

• Fetch the model and deployments.
• Configure Watson OpenScale.
• Create the service provider and subscription for your machine learning service.
• Configure the quality monitor.
• Configure the fairness monitor.
• Configure explainability.

Follow these steps to run the notebook included in the sample project. This notebook sets up monitors for your model, which can also be configured through the user interface. However, it is quicker and less error prone to set them up with a notebook. Take some time to read through the comments in the notebook, which explain the code in each cell.

1. From the Navigation menu , choose Projects > View all projects.

2. Open the AI governance project.

   Note: You might see a guided tour showing the tutorials that are included with this use case. The links in the guided tour will open these tutorial instructions.

3. Click the Assets tab, and then navigate to Notebooks.
   

4. Open the 2-monitor-wml-model-with-watson-openscale notebook.

5. Since the notebook is in read-only mode, click the Edit icon to place the notebook in edit mode.

6. When you imported the project from the Resource hub, the first cell of this notebook contains the project access token. If this notebook does not contain a first cell with a project access token, then to generate the token, from the More menu, select Insert project token. This action inserts a new cell as the first cell in the notebook containing the project token.

7. Under the Insert IBM Cloud API key section, paste your API key in the ibmcloud_api_key field.

8. Click Cell > Run All to run all of the cells in the notebook. Alternatively, you can run the notebook cell by cell if you want to explore each cell and its output.

9. The notebook takes 1 - 3 minutes to complete. You can monitor the progress cell by cell noticing the asterisk "In [*]" changing to a number, for example, "In [1]".

10. If you encounter any errors during the notebook run, try these troubleshooting tips:

    • Click Kernel > Restart & Clear Output to restart the kernel, and then run the notebook again.

    • Delete any existing Watson OpenScale deployments, and provision a new service instance.

    • Verify that you created the AI use case, deployment space, and deployment name in the Build and deploy a model tutorial by copying and pasting the specified artifact name exactly with no leading or trailing spaces.

To preview this task, watch the video beginning at 03:40.

Follow these steps to download holdout data, and use that data to evaluate the model in Watson OpenScale:

1. Click the AI governance project in the navigation trail.
   

2. On the Assets tab, click Data > Data assets.

3. Click the Overflow menu for the GoldenBank_HoldoutData.csv data asset, and choose Download. To validate that the model is working as required, you need a set of labeled data, which was held out from model training. This CSV file contains that holdout data.

4. Launch Watson OpenScale. From the Navigation Menu , choose Services > Service instances.

5. Open your Watson OpenScale instance. If prompted, log in using the same credentials that you used to sign up for Cloud Pak for Data.

6. On the Watson OpenScale service instance page, click Launch Watson OpenScale.

7. On the Insights dashboard, click the Mortgage Approval Model Deployment tile.

8. From the Actions menu, select Evaluate now.

9. From the list of import options, select from CSV file.

10. Drag the Golden Bank_HoldoutData.csv data file you downloaded from the project into the side panel.

11. Click Upload and evaluate.

To preview this task, watch the video beginning at 04:44.

The Watson OpenScale quality monitor generates a set of metrics to evaluate the quality of your model. You can use these quality metrics to determine how well your model predicts outcomes. When the evaluation that uses the holdout data completes, follow these steps to observe the model quality or accuracy:

1. In the left navigation panel, click the Insights dashboard icon .

2. Locate the Mortgage Approval Model Deployment tile. Notice that the deployment has 0 issues, and that both Quality and Fairness tests passed, meaning that the model met the thresholds that are required of it.

3. Click the Mortgage Approval Model Deployment tile to see more detail.

4. In the Quality section, click the Configure icon . Here you can see that the quality threshold that is configured for this monitor is 70% and that the measurement of quality being used is area under the ROC curve.

5. Click Go to model summary to return to the model details screen.

6. In the Quality section, click the Details icon to see the model quality detailed results. Here you see a number of quality metric calculations and a confusion matrix showing correct model decisions along with false positives and false negatives. The calculated area under the ROC curve is 0.9 or higher, which exceeds the 0.7 threshold, so the model is meeting its quality requirement.

7. Click Mortgage Approval Model Deployment in the navigation trail to return to the model details screen.

To preview this task, watch the video beginning at 06:01.

The Watson OpenScale fairness monitor generates a set of metrics to evaluate the fairness of your model. You can use the fairness metrics to determine if your model produces biased outcomes. Follow these steps to observe the model fairness:

1. In the Fairness section, click the Configure icon . Here you see that the model is being reviewed to ensure that applicants are being treated fairly regardless of their gender. Women are identified as the monitored group for whom fairness is being measured and the threshold for fairness is to be at least 80%. The fairness monitor uses the disparate impact method to determine fairness. Disparate impact compares the percentage of favorable outcomes for a monitored group to the percentage of favorable outcomes for a reference group.

2. Click Go to model summary to return to the model details screen.

3. In the Fairness section, click the Details icon to see the model fairness detailed results. Here you see the percentage of male and female applicants who are being automatically approved, along with a fairness score of over 100%, so the model performance far exceeds the 80% fairness threshold required.

4. Note the identified data sets. To ensure that the fairness metrics are most accurate, Watson OpenScale uses perturbation to determine the results where only the protected attributes and related model inputs are changed while other features remain the same. The perturbation changes the values of the feature from the reference group to the monitored group, or vice-versa. These additional guardrails are used to calculate fairness when the "balanced" data set is used, but you can also view the fairness results using only payload or model training data. Since the model is behaving fairly, you don't need to go into additional detail for this metric.

   

5. Click the Mortgage Approval Model Deployment navigation trail to return to the model details screen.

To preview this task, watch the video beginning at 07:42.

It is also important to understand how the model came to its decision. This understanding is required both to explain decisions to people involved in the loan approval and to ensure model owners that the decisions are valid. To understand these decisions, follow these steps to observe the model explainability:

1. In the left navigation panel, click the Explain a transaction icon .

2. Select Mortgage Approval Model Deployment to see a list of transactions.

3. For any transaction, click Explain under the Actions column. Here you see the detailed explanation of this decision. You will see the most important inputs to the model along with how important each was to the end result. Blue bars represent inputs that tended to support the model's decision while red bars show inputs that might have led to another decision. For example, an applicant might have enough income to otherwise be approved but their poor credit history and high debt together lead the model to reject the application. Review this explanation to become satisfied about the basis for the model decision.

4. (Optional) If you want to delve further into how the model made its decision, click the Inspect tab. Use the Inspect feature to analyze the decision to find areas of sensitivity where a small changes to a few inputs would result in a different decision, and you can test the sensitivity yourself by overriding some of the actual inputs with alternatives to see whether these would impact the result.

To preview this task, watch the video beginning at 09:21.

Follow these steps to change the status of the AI use case in the model inventory and approve the model:

1. Return to Cloud Pak for Data, and from the Navigation Menu , choose Catalogs > AI use cases.

2. Open the Mortgage Approval Model Use Case.

3. Click the Lifecycle tab to see that the model is now in the Validate stage.

4. Click the Edit icon next to Status, select Validation complete, and click Update.

5. Return to the Watson OpenScale Insights dashboard.

6. Click the Mortgage Approval Model Deployment tile.

7. From the Actions menu, select Approve for production, and then click Approve. This action conveys to the AI operations team that they can now deploy the model to a designated production space.

8. From the Navigation menu , choose Catalogs > AI use cases.

9. For the Mortgage Approval model use case, click View details.

10. Click the Lifecycle tab. Under Model tracking, the AI use case now displays as evaluated and approved in the Validate stage.

11. View the Mortgage Approval Model Deployment to see the factsheet captured by Watson OpenScale.

12. Close the model deployment factsheet.