Python client examples (Decision Optimization)

You can deploy a Decision Optimization model, create and monitor jobs, and get solutions using the Watson Machine Learning Python Client.

For more information, see Watson Machine Learning Python client documentation.

See also the Deploying a DO model with WML, RunDeployedModel, and ExtendWMLSoftwareSpec notebooks in the jupyter folder of the DO-samples. Select the relevant product and version subfolder.

The Deploying a DO model with WML sample shows you how to deploy a Decision Optimization model, create and monitor jobs, and get solutions using the Watson Machine Learning Python Client. This notebook uses the diet sample for the Decision Optimization model and takes you through the whole procedure without using the Decision Optimization experiment UI.

The RunDeployedModel shows you how to run jobs and get solutions from an existing deployed model. This notebook uses a model saved for deployment from a Decision Optimization experiment UI scenario.

The ExtendWMLSoftwareSpecnotebook shows you how to extend the Decision Optimization software specification within Watson Machine Learning to enable you to use your own pip package to add custom code and deploy it in your model and send jobs to it.