Machine learning has revolutionized the capability of businesses to create personalized experiences via real-time, individual predictions and recommendations. But what happens when one must make thousands of decisions for thousands of individuals at the same time?
At Dia&Co, a plus-size women’s styling service, we recently faced such an obstacle when building out a brand new product line for the business. This talk will explore how we combined modern machine learning with classical operations research techniques to scale personalization in the face of constraints inherent to a retail business.
The basics of operations research will be introduced before demonstrating how to solve a simple version of our real-world problem using all open source libraries. I will then reveal the gory details of productionizing this work, from testing to gracefully handling failures of convergence. Finally, I will cover the journey from the coldest of starts, with zero data, to synthesizing machine learning with the operations research problem.
Ethan is a Machine Learning Engineer at Dia&Co, a plus-sized women's retailer. He lead a team of scientists, engineers, and analysts focused on Styling and Customer Growth algorithms. He got his start in data science working at Birchbox. Prior to that, he earned a PhD in Physics from Columbia University where he studied unconvential superconductors.
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