Deployment steps (Decision Optimization)

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.

Overview

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

  1. Authenticate and create a space. See Rest API example.
  2. Deploy your model with common data. This can be done from the user interface (see Deploying from the user interface) or by following the steps described in Model deployment. See also this REST API example.
  3. 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. See configurations.

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