Decision Optimization experiments
If you use the Decision Optimization experiment UI, you can create and solve models, produce reports, compare scenarios, and save models ready for deployment with Watson Machine Learning.
- 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
- Machine Learning service
- You must have a Machine Learning 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.
The Decision Optimization experiment UI has different views in which you can select data, create models, solve different scenarios, and visualize the results. You can also save your scenarios for deployment from the experiment UI interface. You can access the following views from the side tabs:
- Overview
- Prepare data
- Build model
- Explore solution
- Visualization
For the Prepare data, Build model, and Explore solution views, you can organize your screen as full-screen or as a split-screen. To do so, hover over one of the view tabs for a second or two. A menu then appears where you can select Full Screen, Left or Right. For example, if you choose Left for the Prepare data view, and then choose Right for the Explore solution view, you can see both these views on the same screen.
With the Decision Optimization experiment UI, you can create several scenarios, with different data sets and optimization models. Thus, you can create and compare different scenarios and see what impact changes can have on a problem. For example, if you want to compare the impact of changes to data, you can make two scenarios with different data, run the models, and compare the solutions.
- a set of data (in tables)
- a model
- a solution (when the model is solved)
For more information about scenarios, see Scenarios in a Decision Optimization experiment.
For a step-by-step guide to build, solve and deploy a Decision Optimization model, by using the user interface, see the Quick start tutorial with video.