Operationalizing Machine Learning for Lyft's Business Platforms
In the old worldview of Lyft business platforms, models were simply extensions of simple business rules handcrafted by analysts. Our infrastructure, operations, and product simply had to be re-tailored for a world where decisions are made automatically by machine learning (ML) models. This talk will focus on the operational realities of scaling ML-based decisioning beyond the technical infrastructural challenges in the context of fraud, enterprise, and customer support. Within these “business platforms,” we explore the unique challenges of working with messy data sources that suffer from feedback loops and adaptive adversaries, building ML-based workflows that improve support agent efficiency, and developing scalable infrastructure that can serve the needs of diverse teams.
Hao Yi is a Research Scientist at Lyft. On the fraud team, Hao Yi helped combat transaction and driver fraud that threatened to cripple Lyft's product. As the Science lead for the past two years, he championed new approaches and led ML improvements that reduced fraud by huge margins without hurting growth and helped Lyft achieve best-in-class status in chargeback rate within the ridesharing industry. On Support Experience, Hao Yi developed deep learning models for support ticket classification and routing that led to massive reductions in false positive tickets and manual ticket re-routing. He’s now working on the next-generation driver positioning optimization framework on the Driver Engagement team. Before Lyft, Hao Yi worked on drone traffic management with NASA Ames as a graduate at Stanford.