Deploying a Decision Optimization model by using the user interface
You can save a model for deployment in the Decision Optimization experiment UI and promote it to your deployment space.
Before you begin
About this task
When you're satisfied with its results, reliability, and performance, you can deploy a model from the Decision Optimization experiment UI.
- From the Decision Optimization experiment UI, save your model scenario as a Model in your Project.
- Promote your Model to your deployment space.
- From your deployment space, create a new deployment.
- You can then create and run jobs to your deployed model.
Procedure
To save your model for deployment:
To promote your model to your deployment space:
To create a new deployment:
Results
You can access information about your deployment on the Deployments tab of your model in your deployment space.
Creating and running Decision Optimization jobs
You can create and run jobs to your deployed model.
Procedure
- Return to your deployment space by using the navigation path and (if the data pane isn't already open) click the data icon to open the data pane. Upload your input data tables, and solution and kpi output tables here. (You must have output tables defined in your model to be able to see the solution and kpi values.)
- Open your deployment model, by selecting it in the Deployments tab of your deployment space and click New job.
- Define the details of your job by entering a name, and an optional description for your job and click Next.
- Configure your job by selecting a hardware specification and
Next. You can choose to schedule your job here, or leave the default schedule option off and click Next. You can also optionally choose to turn on notifications or click Next.
- Choose the data that you want to use in your job by clicking Select the source for each of your input and output tables. Click Next.
- You can now review and create your model by clicking
Create. When you receive a successful job creation message, you can then view it by opening it from your deployment space. There you can see the run status of your job.
- Open the run for your job. Your job log opens and you can also view and copy the payload information.
Results
You can create and monitor jobs, and get solutions by using the watsonx.ai Runtime Python client. See the RunDeployedModel notebook in the DO-samples. Select the relevant product and version subfolder.