Ruth Luscombe
My background is in applied mathematics and data science. I have a PhD and 15 years industry experience. Currently working as a data scientist at a fintech startup where my interest is in building maintainable and robust models for deployment.
Session
This is an overview of the tips and tricks I learned while bringing a mathematical optimization model from concept to prototype to production.
At its core, the math model is a multi-dimensional knapsack problem, with tens of thousands of items moving between warehouses with up to a hundred user-defined constraints. The model is solved with Google's OR-Tools and is deployed to AWS Lambda.
Some of the important ideas I want to share are how to build a test suite to ensure that a model of this type is internally consistent and how to handle errors-as-data for your end users. I also learned a really nice way to configure logging in python.