Table of contents

Ways to use Decision Optimization

To build Decision Optimization models, you can create Python notebooks using DOcplex, a native Python API for Decision Optimization or use the Decision Optimization experiment UI which has more benefits and features.

Ways to use Decision Optimization

There are different ways you can use Decision Optimization, depending on your skills and expertise:

  • With Python notebooks using DOcplex, a native Python API for Decision Optimization. This requires Operational Research (OR) modeling expertise to create variables, objectives, and constraints to represent your problem.
  • through the Decision Optimization experiment UI, which facilitates workflow and provides many other features to help you create and run (solve) scenarios:
  • For batch deployment with Watson Machine Learning see Deploying Decision Optimization models with Watson Machine Learning.
    Figure 1. Modeling and solving with the Decision Optimization model builder
    Chart showing workflow and different ways to use the model builder

Decision Optimization experiment UI advantages

The following table highlights how you can perform different functions both with and without the Decision Optimization experiment UI. As you can see, there are more advantages when using the Decision Optimization experiment UI. See Model builder features.

Table 1. Decision Optimization with the experiment UI
To... Jupyter notebook Decision Optimization experiment UI
Python OPL models Modeling Assistant

Manage data

Import data from Projects

Import data from Projects and edit data in the Prepare data view

Import data from Projects and edit data in the Prepare data view

Import data from Projects and edit data in the Prepare data view

Relationships in your data are intelligently deduced.

Formulate and run optimization models

Create a model formulation from scratch in a Python notebook. using the DOcplex API.

With notebooks individual cells can be run interactively which facilitates debugging.

Create a model formulation from scratch in Python.

Import and view a model formulation from a notebook or file.

Edit the imported Python model directly.

Export your model as a notebook. With notebooks individual cells can be run interactively which facilitates debugging.

Create a model formulation from scratch in OPL.

Import and view a model formulation from an OPL file.

Edit the imported OPL model directly.

Create a model formulation from scratch by selecting from the proposed options expressed in natural language.

Import and view a Modeling Assistant model formulation from a scenario.

Edit the imported model directly.

Create and compare multiple scenarios

Write Python code to handle scenario management.

Create and manage scenarios to compare different instances of model, data and solutions. See Scenario pane and Overview.

Create and share reports

Create reports in your notebooks using Python data visualization tools.

Rapidly create reports in the Visualization view using widgets, pages and a JSON editor.

Download your report as a JSON file to share with your team.

Deploy a model

Deploy notebooks using Watson Machine Learning REST API or Python client

Simply select the scenario you want to save ready for promotion to the deployment space. See Deploying a Decision Optimization model using the user interface.

Deploy your Decision Optimization prescriptive model and associated common data once and then submit job requests to this deployment with only the related transactional data. This can be achieved using the Watson Machine Learning REST API or using the Watson Machine Learning Python client.

Learn more