What's happening in the sample pipeline?

The sample pipeline gets training data, trains a machine learning model using the AutoAI tool, and selects the best pipeline to save as a model. The model is then copied to a deployment space where is deployed.

The sample illustrates how you can automate an end-to-end flow to make the lifecycle easier to run and monitor.

The sample pipeline looks like this:

The tutorial steps you through this process:

1. Setting up to run the sample
2. Creating the sample pipeline
3. Running the sample
4. Reviewing the results
5. Exploring the sample nodes and configuration

Prerequisites

To run this sample, you must first create:

• A project, where you can run the sample pipeline.
• A deployment space, where you can view and test the results. The deployment space is required to run the sample pipeline.

Preview creating and running the sample pipeline

Watch this video to see how to create and run a sample pipeline.

Creating the sample pipeline

Create the sample pipeline in the Pipelines editor.

1. Open the project where you want to create the pipeline.

2. From the Assets tab, click New task > Automate model lifecycle.

3. Enter a unique name for the pipeline. For example, enter Bank marketing sample.

4. Click Create to open the canvas.

5. Click the Samples tab, and select the Orchestrate an AutoAI experiment.

Running the sample pipeline

To run the sample pipeline:

1. Click Run pipeline on the canvas toolbar, then choose Trial run.

2. Select a deployment space when prompted to provide a value for the deployment_space pipeline parameter.

   1. Click Select Space.

   2. Expand the Spaces section.

   3. Select your deployment space.

   4. Click Choose.

3. Provide an API key if this occasion is your first time running a pipeline. 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 the pipeline.

Reviewing the results

When the pipeline run completes, you can view the output to see the results.

Open the deployment space you specified as part of the pipeline. You will see the new deployment in the space:

If you want to test the deployment, use the deployment space Test page to submit payload data in JSON format and get a score back. For example, click the JSON tab and enter this input data:

 {"input_data": [{"fields": ["age","job","marital","education","default","balance","housing","loan","contact","day","month","duration","campaign","pdays","previous","poutcome"],"values": [["30","unemployed","married","primary","no","1787","no","no","cellular","19","oct","79","1","-1","0","unknown"]]}]}

When you click Predict, the model generates output with a confidence score for the prediction of whether a customer will subscribe to a term deposit promotion.

In this case, the prediction of "no" is accompanied by a confidence score of close to 95%, predicting that the client most likely will not subscribe to a term deposit.

Exploring the sample nodes and configuration

Get a deeper understanding of how the sample nodes were configured to work in concert in the pipeline sample.