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Decision Optimization deployment steps

Deployment steps

With IBM Watson Machine Learning you can 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 deployment can be achieved by using the Watson Machine Learning REST API, watson.ai Python client or the IBM Cloud Pak for Data as a Service Command Line Interface.

See REST API example for a full code example. See Python client examples for a link to a Python notebook available from the Resource hub.


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 by using the https://dataplatform.cloud.ibm.com user interface or with the REST API.
  3. Deploy your model with common data. This deployment can be done from the user interface (see Deploying from the user interface) or by following the steps that are described in Model deployment. See also this REST API example.
  4. Deploy your model with common data. This is described in Model deployment. See also this REST API example.
  5. 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, large, and extra large.

Table 1. T-shirt sizes for Decision Optimization
Definition Name Description
2 vCPU and 8 GB S Small
4 vCPU and 16 GB M Medium
8 vCPU and 32 GB L Large
16 vCPU and 64 GB XL Extra Large
See also Running jobs.

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