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Creating a Decision Optimization experiment
Last updated: Nov 21, 2024
Creating a Decision Optimization experiment

This example shows you how to create a Decision Optimization experiment.

Before you begin

Requirements
To edit and run Decision Optimization models, you must have the following prerequisites:
Admin or Editor roles
You must have Admin or Editor roles in the project. Viewers of shared projects can only see experiments, but cannot modify or run them
watsonx.ai Runtime service
You must have a watsonx.ai Runtime service that is associated with your project. You can add one when you create a Decision Optimization experiment.
Deployment space
You must have a deployment space that is associated with your Decision Optimization experiment. You can choose a deployment space when you create a Decision Optimization experiment.

About this task

You can create a Decision Optimization experiment from scratch or from a sample file.

Procedure

To create a Decision Optimization experiment:

  1. Open your project or create an empty project.
  2. Select the Assets tab.
  3. Select New asset > Solve optimization problems in the Work with models section.
  4. In the Create a Decision Optimization experiment window that opens, enter a name to create a model from scratch. You can alternatively choose Local file and select a .zip file to upload an existing experiment.
  5. If you haven't already associated a watsonx.ai Runtime service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
  6. Click New deployment space, enter a name, and click Create (or select an existing space from the drop-down menu).
  7. Click Create.

Results

A Decision Optimization experiment is created that contains Scenario 1. (Alternatively, if you choose a local file, the experiment scenarios open).

For more information about scenarios, see Scenarios in a Decision Optimization experiment.

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more