Machine learning, causal inference, and reinforcement learning have played a key role in scaling up and personalizing Lyft decisions for growth, ranging from an ad exposure, a landing page for user onboarding, an email to be sent to a user, and a coupon/bonus to be dropped to rider/driver. In this talk, we’ll showcase a suite of growth engines powering Lyft’s rider, driver, and business growth, including user acquisition platform, communications platform, and incentives platform. We’ll discuss challenges in building real-world large-scale ML systems for business decisions, and share some learnings and best practices that can be ported to other industries beyond rideshare.
Session Summary
Scaling up Lyft’s Growth via Causal ML and Bandits
MLconf 2022 San Francisco
Dr. Lei Tang
Fabi.ai
Founder and CTO
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