Quick start: Build and deploy a machine learning model with AutoAI

You need a project to store the data and the AutoAI experiment. You can use your sandbox project or create a project.

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

2. Open your sandbox project. If you want to use a new project:

   1. Click New project.

   2. Select Create an empty project.

   3. Enter a name and optional description for the project.

   4. Choose an existing object storage service instance or create a new one.

   5. Click Create.

3. When the project opens, click the Manage tab and select the Services and integrations page.

   1. On the IBM services tab, click Associate service.

   2. Select your watsonx.ai Runtime instance. If you don't have a watsonx.ai Runtime service instance provisioned yet, follow these steps:

      1. Click New service.

      2. Select watsonx.ai Runtime.

      3. Click Create.

      4. Select the new service instance from the list.

   3. Click Associate service.

   4. If necessary, click Cancel to return to the Services & Integrations page.

For more information or to watch a video, see Creating a project.

To preview this task, watch the video beginning at 00:08.

Now that you have a project, you are ready to build and train the model using AutoAI. Follow these steps to create the AutoAI experiment, review the model pipelines, and select a pipeline to save as a model:

1. Click the Assets tab in your project, and then click New asset > Build machine learning models automatically.

2. On the Build machine learning models automatically page, complete the basic fields:

   1. Click the Resource hub sample panel.

   2. Select Bank marketing sample data, and click Next. The project name and description will be filled in for you.

   3. Confirm that the watsonx.ai Runtime service instance that you associated with your project is selected in the watsonx.ai Runtime Service Instance field.

3. Click Create.

4. In this sample AutoAI experiment, you will see that the Bank marketing sample data is already selected for your experiment.

   

5. Review the preset experiment settings. Based on the data set and the selected column to predict, AutoAI analyzes a subset of the data and chooses a prediction type and metric to optimize. In this case, the prediction type is Binary Classification, the positive class is Yes, and the optimized metric is ROC AUC & run time.

6. Click Run experiment. As the model trains, you see an infographic that shows the process of building the pipelines.
   

   For a list of algorithms, or estimators, available with each machine learning technique in AutoAI, see: AutoAI implementation detail.

7. After the experiment run is complete, you can view and compare the ranked pipelines in a leaderboard.

   

8. You can click Pipeline comparison to see how they differ.

   

9. Click the highest ranked pipeline to see the pipeline details.

10. Click Save as, select Model, and click Create. This saves the pipeline as a model in your project.

11. When the model is saved, click the View in project link in the notification to view the model in your project. Alternatively, you can navigate to the Assets tab in the project, and click the model name in the Models section.

To preview this task, watch the video beginning at 04:57.

Before you can deploy the model, you need to promote the model to a deployment space. Follow these steps to promote the model to a deployment space to deploy the model:

1. Click the Promote to deployment space icon .

2. Choose an existing deployment space. If you don't have a deployment space:

   1. Click Create a new deployment space.

   2. Provide a space name and optional description.

   3. Select a storage service.

   4. Select a machine learning service.

   5. Click Create.

   6. Click Close.

3. Select your new deployment space from the list.

4. Select the Go to the model in the space after promoting it option.

5. Click Promote.

   Note: If you didn't select the option to go to the model in the space after promoting it, you can use the navigation menu to navigate to Deployments to select your deployment space and model.

6. With the model open, click New deployment.

   1. Select Online as the Deployment type.

   2. Specify a name for the deployment.

   3. Click Create.

7. When the deployment is complete, click the deployment name to view the deployment details page.

To preview this task, watch the video beginning at 06:22.

Now that you have the model deployed, you can test that that online deployment using the user interface or through the watsonx.ai Runtime APIs. Follow these steps to use the user interface to test the model with new data:

1. Click the Test tab. You can test the deployed model from the deployment details page in two ways: test with a form or test with JSON code.

2. Click the JSON input tab, copy the following test data, and paste it to replace the existing JSON text:

   {
      "input_data": [
         {
            "fields": [
                  "age",
                  "job",
                  "marital",
                  "education",
                  "default",
                  "balance",
                  "housing",
                  "loan",
                  "contact",
                  "day",
                  "month",
                  "duration",
                  "campaign",
                  "pdays",
                  "previous",
                  "poutcome"
               ],
            "values": [
                  [
                  27,
                  "unemployed",
                  "married",
                  "primary",
                  "no",
                  1787,
                  "no",
                  "no",
                  "cellular",
                  19,
                  "oct",
                  79,
                  1,
                  -1,
                  0,
                  "unknown"
                  ]
               ]
         }
      ]
   }
   

3. Click Predict to predict whether a customer with the specified attributes is likely to sign up for a particular kind of account. The resulting prediction indicates that this customer has a high probability of not enrolling in the marketing promotion.

4. Click the X to close the Prediction results window.

Now that you have tested the deployed model with a single prediction, you can create a batch deployment to score multiple records at the same time.