To preview this task, watch the video beginning at 02:26.

The sample project includes several assets including a connection, data definition, two DataStage flows, and a pipeline. Follow these steps to view those assets:

1. Click the Assets tab in the Orchestrate an AI pipeline project, and then view All assets.

2. All of the data assets that are used in the DataStage flows and the pipeline are stored in a Data Fabric Trial - Db2 Warehouse connection in the AI_MORTGAGE schema. The following image shows the assets from that connection:

   

3. The Integrate Mortgage Data DataStage flow integrates data about each mortgage applicant, including personally identifiable information, with their application details, credit scores, status as a commercial buyer, and finally the prices of each applicant’s chosen home, and then creates a sequential file with the name Mortgage_Data.csv in the project containing the joined data. The following image shows the Integrate Mortgage Data DataStage flow.

   Tip: If you don't see any DataStage flows, then go back to view your service instances to verify your DataStage instance provisioned successfully. See Provision the necessary services.

   

4. The Integrate Mortgage Approvals DataStage flow uses the output from the first DataStage flow (Mortgage_Data.csv) and further enriches the data by integrating information about each mortgage application approval. The resulting data set is saved to the project with the name Mortgage_Data_with_Approvals.csv. The following image shows the Integrate Mortgage Approvals DataStage flow:

   

5. The Definition_Mortgage_Data data definition for the Mortgage_Data_with_Approvals.csv data asset is created by the Integrate Mortgage Approvals DataStage flow. The following image shows the data definition:

   

To preview this task, watch the video beginning at 04:00.

The sample project includes an Orchestration Pipelines, which automates the following tasks:

• Run two existing DataStage jobs.

• Create an AutoAI experiment.

• Run the AutoAI experiment and save the best performing model that uses the resulting output file from the DataStage job as the training data.

• Create a deployment space.

• Promote the saved model to the deployment space.

Follow these steps to explore the pipeline:

1. From the Assets tab in the Orchestrate an AI pipeline project, view All assets.

2. Click Mortgage approval pipeline to open the pipeline.

3. In the beginning section of the pipeline, two DataStage jobs (Integrate Mortgage Data and Integrate Mortgage Approvals) run in sequence to combine various tables from the Db2 Warehouse on Cloud connection into a cohesive labeled data set that is used as the training data for the AutoAI experiment.

4. Double-click the Check Status node to see the condition. This condition is a decision point in the pipeline to confirm the completion of the first DataStage job with a value of either Completed or Completed With Warnings. Click Cancel to return to the pipeline.

5. Double-click the Create AutoAI experiment node to see the settings. This node creates an AutoAI experiment with the settings.

   1. Review the values for the following settings:

      • AutoAI experiment name

      • Scope

      • Prediction type

      • Prediction column

      • Positive class

      • Training data split ratio

      • Algorithms to include

      • Algorithms to use

      • Optimize metric

   2. Click Cancel to close the settings.

6. Double-click the Run AutoAI experiment node to see the settings. This node runs the AutoAI experiment that is created from the Create AutoAI experiment node that uses the output from the Integrate Mortgage Approval DataStage job as the training data.

   1. Review the values for the following settings:

      • AutoAI experiment

      • Training Data Assets

      • Model name prefix

   2. Click Cancel to close the settings.

7. Between the Run AutoAI experiment and Create Deployment Space nodes, double-click the Do you want to deploy model? node to see the condition. The value of True for this condition is a decision point in the pipeline to continue to create the deployment space. Click Cancel to return to the pipeline.

8. Double-click the Create Deployment Space node to see the settings. This node creates a new deployment space with the specified name, and requires input for your Cloud Object Storage and watsonx.ai Runtime services.

   1. Review the value for the New space name setting.

   2. For the New space COS Instance CRN field, select your Cloud Object Storage instance from the list.

   3. For the New space WML Instance CRN field, select your watsonx.ai Runtime instance from the list.

   4. Click Save.

9. Double-click the Promote Model to Deployment Space node to see the settings. This node promotes the best model from the Run AutoAI experiment node to the deployment space created from the Create Deployment Space node.

   1. Review the values for the following settings:

      • Source Assets

      • Target

   2. Click Cancel to close the settings.

To preview this task, watch the video beginning at 06:23.

The pipeline creates the model, creates a deployment space, and then promotes it to a deployment space. You need to add a node to create an online deployment. Follow these steps to edit the pipeline to automate creating an online deployment:

1. Add the Create Online Deployment node to the canvas:

   1. Expand the Create section in the node palette.

   2. Drag the Create online deployment node onto the canvas, and drop the node after the Promote Model to Deployment Space node.

2. Hover over the Promote Model to Deployment Space node to see the arrow. Connect the arrow to the Create online deployment node.

   Note: The node names in your pipeline might differ from the following animated image.

   

3. Connect the Create online deployment for promoted model comment to the Create online deployment node by connecting the circle on the comment box to the node.

   Note: The node names in your pipeline might differ from the following animated image.

   

4. Double-click the Create online deployment node to see the settings.

5. Change the node name to Create Online Deployment.

6. Next to ML asset, click Select from another node from the menu.

   

7. Select the Promote Model to Deployment Space node from the list. The node ID winning_model is selected.

8. For the New deployment name, type mortgage approval model deployment.

9. For Creation Mode, select Overwrite.

10. Click Save to save the Create Online Deployment node settings.

To preview this task, watch the video beginning at 07:38.

Now that the pipeline is complete, follow these steps to run the pipeline:

1. From the toolbar, click Run pipeline > Trial run.

2. On the Define pipeline parameters page, select True for the deployment.

   • If set to True, then the pipeline verifies the deployed model and scores the model.

   • If set to False, then the pipeline verifies that the model was created in the project by the AutoAI experiment, and reviews the model information and training metrics.

3. If this occasion is your first time running a pipeline, you are prompted to provide an API key. Pipeline assets use your personal IBM Cloud API key to run operations securely without disruption.

   • If you have an existing API key, click Use existing API key, paste the API key, and click Save.

   • If you don't have an existing API key, click Generate new API key, provide a name, and click Save. Copy the API key, and then save the API key for future use. When you're done, click Close.

4. Click Run to start running the pipeline.

5. Scroll through consolidated logs while the pipeline is running. The trial run might take up to 10 minutes to complete.

6. As each operation completes, select the node for that operation on the canvas.

7. On the Node Inspector tab, view the details of the operation.

8. Click the Node output tab to see a summary of the output for each node operation.

To preview this task, watch the video beginning at 09:48.

The pipeline created several assets. Follow these steps to view the assets:

1. Click the Orchestrate an AI pipeline project name in the navigation trail to return to the project.

   

2. On the Assets tab, view All assets.

3. View the data assets.

   1. Click the Mortgage_Data.csv data asset. The DataStage job created this asset.

   2. Click the project name in the navigation trail to return to the Assets tab.

   3. Click the Mortgage_Data_with_Approvals.csv data asset. The DataStage job created this asset.

   4. Click the project name in the navigation trail to return to the Assets tab.

4. View the model.

   1. Click the machine learning model asset beginning with mortgage_approval_best_model. The AutoAI experiment generated several model candidates, and chose this as the best model.

   2. Scroll through the model information.

   3. Click the project name in the navigation trail to return to the Assets tab.

5. Click the Jobs tab in the project to see information about the two DataStage jobs and one Pipeline job runs.

6. From the Navigation Menu , choose Deployments.

7. Click the Spaces tab.

8. Click the Mortgage approval deployment space.

9. Click the Assets tab, and see the deployed model beginning with mortgage_approval_best_model.

10. Click the Deployments tab.

11. Click mortgage approval model deployment to view the deployment.

    1. View the information on the API reference tab.

    2. Click the Test tab.

    3. Click the JSON input tab, and replace the sample text with the following JSON text.

       {
          "input_data": [
              {
                      "fields": [
                              "ID",
                              "NAME",
                              "STREET_ADDRESS",
                              "CITY",
                              "STATE",
                              "STATE_CODE",
                              "ZIP_CODE",
                              "EMAIL_ADDRESS",
                              "PHONE_NUMBER",
                              "GENDER",
                              "SOCIAL_SECURITY_NUMBER",
                              "EDUCATION",
                              "EMPLOYMENT_STATUS",
                              "MARITAL_STATUS",
                              "INCOME",
                              "APPLIEDONLINE",
                              "RESIDENCE",
                              "YRS_AT_CURRENT_ADDRESS",
                              "YRS_WITH_CURRENT_EMPLOYER",
                              "NUMBER_OF_CARDS",
                              "CREDITCARD_DEBT",
                              "LOANS",
                              "LOAN_AMOUNT",
                              "CREDIT_SCORE",
                              "CRM_ID",
                              "COMMERCIAL_CLIENT",
                              "COMM_FRAUD_INV",
                              "FORM_ID",
                              "PROPERTY_CITY",
                              "PROPERTY_STATE",
                              "PROPERTY_VALUE",
                              "AVG_PRICE"
                      ],
                      "values": [
                              [
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      "Bachelor",
                                      "Employed",
                                      null,
                                      144306,
                                      null,
                                      "Owner Occupier",
                                      15,
                                      19,
                                      2,
                                      7995,
                                      1,
                                      1483220,
                                      437,
                                      null,
                                      false,
                                      false,
                                      null,
                                      null,
                                      null,
                                      111563
                              ],
                              [
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      null,
                                      "High School",
                                      "Employed",
                                      null,
                                      45283,
                                      null,
                                      "Private Renting",
                                      11,
                                      13,
                                      1,
                                      1232,
                                      1,
                                      7638,
                                      706,
                                      null,
                                      false,
                                      false,
                                      null,
                                      null,
                                      null,
                                      547262
                              ]
                      ]
              }
          ]
       }
       

    4. Click Predict. The results show that the first applicant would not be approved and the second applicant will be approved.