Deploying and executing a model
Describes the deployment and execution features in Decision Optimization.
When you are satisfied with a Decision Optimization model that solves successfully with expected results, you can deploy it for use by an application inside Watson Studio. Before you deploy, you need to save a model scenario as a deployable model. You then use IBM Watson Machine Learning to specify how to deploy the model.
Once the model is deployed, you submit jobs to it using a REST API.