Abstract: 

Getting the full benefit of a machine learning model can be difficult, and getting users to leverage and adopt it can be even more so. Although we can turn data into forecasts and insights, these reveal what’s happened in the past and what’s likely to happen next. This can still leave users asking the most important question: What should we do? For that, we need help from optimization to give business users the tools to take full advantage of our machine learning models.

When your decisions involve complex trade-offs among various business objectives and have an astronomical number of viable solutions, only mathematical optimization has the power to find the best or optimal solution – which can be used to make optimal business decisions.

The analytical journey from data to decisions may involve adding a new skill to your analytical toolbox. Join Dr. Alison Cozad for a discussion on,

When and why to ask your business user, ""What are you going to do with these results?""
How mathematical optimization can complement your machine learning models
A combined data science and optimization python example and demo with Gurobi

Bio: 

Dr. Alison Cozad holds a Ph.D. in Chemical Engineering from Carnegie Mellon University where she leveraged mixed-integer and semi-infinite optimization methods to improve machine learning algorithms. Prior to joining Gurobi, she held multiple roles at ExxonMobil, including as a Senior Data Science Lead and Real-time Optimization Engineer.

In her free time, Alison loves making things from CNC woodworking to electronics to cheese making to sock puppetry.