65 min listen
Causal Models in Practice at Lyft with Sean Taylor - #486
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Causal Models in Practice at Lyft with Sean Taylor - #486
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
40 minutes
Released:
May 24, 2021
Format:
Podcast episode
Description
Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. The complete show notes for this episode can be found at twimlai.com/go/486.
Released:
May 24, 2021
Format:
Podcast episode
Titles in the series (100)
Machine Teaching for Better Machine Learning with Mark Hammond - TWiML Talk #43: Today’s show, which concludes the first season of… by The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)