Python client examples

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.

The Python notebook Deploying a Decision Optimization model in Watson Machine Learning, available from the IBM Watson Studio Gallery, illustrates how you can perform the following tasks:
  • Install the Watson Machine Learning client API
  • Create a client instance
  • Prepare your model archive
  • Upload your model on Watson Machine Learning
  • Create a deployment
  • Create and monitor a job with inline data for your deployed model
  • Display the solution

The ExtendWMLSoftwareSpec notebook in the DO-samples on the Decision Optimization GitHub 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.