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Ways to use Decision Optimization for Data Science Experience

Describes how you can use IBM Decision Optimization for Data Science Experience and highlights the features and benefits of using the Model Builder.

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) modelling expertise to create variables, objectives, and constraints to represent your problem.
  • with the Model Builder (an interface which facilitates workflow and provides many other features) together with Python notebooks or files.
  • with the Model Builder together with its Modeling Assistant (which enables you to formulate models in natural language). This requires little to no knowledge of OR and does not require you to write Python code. This feature is currently in Beta version and is available for scheduling and resource assignment problems.
See Requirements and versions for the currently supported version of Python.

Model Builder Features

The Model Builder is an interface which facilitates workflow where you can easily:
  • select and edit the data relevant for your optimization problem
  • run optimization models in a user-friendly environment
  • generate a notebook from your model, work with it as a notebook, then reload it as a model
  • compare multiple scenarios
  • create and share reports
  • save models that are ready for deployment
See The Model Builder interface.

The following table highlights how you can perform different functions both with and without the Model Builder.

Table 1. Decision Optimization with the Model Builder
To... Python notebook Model Builder
Python notebook Modeling Assistant [Beta version]
Manage data Import data from Projects Import data from Projects and edit data in the Prepare Input Data view Import data from Projects and edit data in the Prepare Input Data view.

Relationships in your data are intelligently deduced.

Formulate and run optimization models Create models in notebooks using the DOcplex API.

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

Import and view a model formulation from notebook or file.

Reload notebook or file in the Model Builder to display edits made.

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

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

Edit the 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 panel.
Create and share reports Create reports in your notebooks using Python data visualization tools. Rapidly create reports in the Dashboard using widgets, pages and a JSON editor.

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

Deploy a service

Not available without the Model Builder.

Simply select the scenario you want to save ready for deployment.

Automatic service generation and deployment