Decision Optimization model builder
If you use the Decision Optimization model builder to create your Decision Optimization experiment, you can take advantage of many features in this user-friendly environment. For example, you can create and solve models, produce reports, compare scenarios and save models ready for deployment with Watson Machine Learning.
The Decision Optimization model builder provides you with a interface that facilitates workflow. Here you can:
- Select and edit the data relevant for your optimization problem, see Prepare data view
- Create, import, edit and solve Python models in the Decision Optimization model builder, see Decision Optimization notebook tutorial
- Create, import, edit and solve models expressed in natural language with the Modeling Assistant, see Modeling Assistant tutorial
- Create, import, edit and solve OPL models in the Decision Optimization model builder, see OPL models
- Generate a notebook from your model, work with it as a notebook then reload it as a model, see Generating a notebook from a scenario and Overview
- Visualize data and solutions, see Explore solution view
- Investigate and compare solutions for multiple scenarios, see Scenario pane and Overview
- Easily create and share reports with tables, charts and notes using widgets provided in the Visualization Editor
- Save models that are ready for deployment in Watson Machine Learning, see Scenario pane and Overview
See this table for a comparison of features available with and without the Decision Optimization model builder.
See Decision Optimization model builder views and scenarios for a description of the user interface and scenario management.