With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications.
The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.
In this latest Data Science Central webinar, we will:
Discuss the motivation for using Python in mathematical optimization applications
Help you understand the importance of parameterizing a mathematical optimization model
Review some of the best practices for deploying mathematical optimization models in Python
Speaker:
Juan Orozco Guzman, Optimization Support Engineer- Gurobi Optimization
Hosted by:
Sean Welch, Host and Producer – Data Science Central
