December 20, 2019

231 words 2 mins read

The magic behind your Lyft ride prices: A case study on machine learning and streaming

The magic behind your Lyft ride prices: A case study on machine learning and streaming

Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalabilityallowing the pricing system to be more adaptable to real-world changes.

Talk Title The magic behind your Lyft ride prices: A case study on machine learning and streaming
Speakers Rakesh Kumar (Lyft), Thomas Weise (Lyft)
Conference Strata Data Conference
Conf Tag Big Data Expo
Location San Francisco, California
Date March 26-28, 2019
URL Talk Page
Slides Talk Slides
Video

Lyft is a multidimensional marketplace. Not only does it require a buyer and a seller (a.k.a. a driver and a passenger); it also needs them to be interested at the same time and at the same place. Reacting to events with traditional methodologies is challenging, especially when timely reaction is required to balance the market condition. Thus, data science tools for machine learning and a process that allows for faster deployment is of growing importance to the business. Rakesh Kumar and Thomas Weise discuss how Lyft uses its streaming platform to run dynamic pricing algorithms, allowing the company to be fair to drivers (by say, raising rates when there’s a lot of demand) and to passengers (by offering to return 10 minutes later for a cheaper rate). To accomplish this, the system consumes a massive amount of events from different sources. Topics include:

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