Why use Decision Optimization for Data Science Experience

Drive operational efficiency

Decision optimization combines mathematical and artificial intelligence techniques to help make important business decisions that involve multiple decision variables, trade-off possibilities and complex constraints. Integration with IBM® Data Science Experience lets you combine optimization and machine learning techniques with model management and deployment capabilities. It helps optimize complex business decisions to generate significant return on investment across a wide range of industries.

Easily build, deploy models on IBM Data Science Experience

Data science teams can quickly build and evaluate optimization models in a unified, collaborative environment – using advanced analytics to enhance operational applications. It can also help configure and evaluate proof-of-concept applications quickly and scale them to production easily using the deployment capabilities within IBM Data Science Experience.

Solve a wide range of decision-making problems

IBM Decision Optimization solutions incorporate highly powerful mathematical programming and constraint programming engines to address all classes of business problems across domains. Take advantage of IBM CPLEX® solvers, which many organizations are using to run their mission-critical decision-making applications. IBM CPLEX solvers can solve large, real-world optimization problems – along with the speed required for today's interactive decision optimization applications.

Easily operationalize your projects

IBM Data Science Experience offers data scientists a complete set of the tools drawn from open source and IBM technologies. It gives data science teams access to peer communities, offers scalability and greater security – making the solution highly adaptable to a broad range of applications. Integration of optimization capabilities within Data Science Experience allows teams to quickly scale their proof-of-concept applications and deploy optimization models into production.

Work smarter and faster as a team

Easily create optimization models by coding in Python or using the modeling assistant. Share graphical dashboards with business analysts to validate the benefits of the models. Use powerful visualization features to test multiple scenarios. Deploy the validated model and optimization engine as a microservice – which business users can then access from their applications.

Simplify the process of creating optimization models

Benefit from using a wizard to guide you during creation of optimization models around scheduling and resource assignment. The modeling assistant uses natural language interactions to define goals and constraints for the model with no coding required. The modeling assistant starts with data input and analyzes the appropriate constraints for the problem that you are trying to address.

See how it works

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