Deployment steps

IBM Watson Machine Learning enables you to deploy your Decision Optimization prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This can be achieved using the Watson Machine Learning REST API or using the Watson Machine Learning Python client.

See REST API example for a full code example, and Python client examples for a link to a Python notebook available from the IBM Watson Studio Gallery.


The steps to deploy and submit jobs for a Decision Optimization model are as follows. These steps are detailed in later sections.

  1. Create a Machine Learning service.
  2. Create a deployment space using the user interface or with the REST API.
  3. Deploy your model with common data. This is described in Model deployment. See also this REST API example.
  4. Create and monitor jobs to this deployed model.

Decision Optimization model lifecycle flowchart showing deployment and use steps

The T-shirt size refers to predefined deployment configurations: small, medium and extra large. See configurations.

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