Deploying a Decision Optimization model using the user interface

You can save a model for deployment in the Decision Optimization model builder and promote it to your Watson Machine Learning deployment space.

Before you begin

You must have a deployment space associated with your project.

About this task

Once you're satisfied with its results, reliability, and performance, you can deploy a model inside Decision Optimization model builder using Watson Machine Learning.

There are three main stages for deployment:
  1. From the Decision Optimization model builder, save your model scenario as a Watson Machine Learning model in your Project.
  2. Promote your Watson Machine Learning model to your deployment space.
  3. From your deployment space create a new deployment.
This is detailed in the following procedure.

Procedure

  1. In the Decision Optimization model builder, either from the Scenario or from the Overview pane, click the menu icon Scenario menu icon beside the scenario that you want to deploy, and select Save for deployment
  2. Specify a name for your model and add a description if required, then click Save.
    The model is available in the Models section of your project as a Watson Machine Learning model.
  3. View your saved Watson Machine Learning model in your project.
    You can see a summary with input and output schema. Click Promote to deployment space.
  4. Click the link deployment space in the message you receive that confirms successful promotion. Open your promoted model in the Assets tab of your Deployment space.
    The information panel shows you the Type, Model ID and Software specification.
  5. Click New Deployment.
  6. On the Configure and deploy as batch page, specify a name for your batch deployment.
    Select a Hardware definition. Click Create to create the deployment. Your deployment is listed with status Deployed in your batch deployments.
  7. Select View from the three dots icon in the Actions menu to view your batch deployment.
    Your model is ready to receive requests. You can access the Deployment ID from the information panel.
  8. Open your deployment model

Results

You can access information about your deployment on the Deployments tab of your model in your deployment space.

You can create and monitor jobs, and get solutions using the Watson Machine Learning Python Client. See the RunDeployedModel notebook in the DO-samples. Select the relevant product and version subfolder.