ABOUT THIS PODCAST
Technical interviews about software topics.
since Jul, 2015
IN THIS PODCAST
DoorDash is a logistics company that connects customers, restaurants, and drivers that can move food to its destination. When a customer orders from a restaurant, DoorDash needs to identify the ideal driver for picking up the order from the restaurant and dropping it off with the customer. This process of matching an order to a driver takes in many different factors. Let’s say I order spaghetti from an Italian restaurant. How long does the spaghetti take to prepare? How much traffic is there in different areas of the city? Who are the different drivers who could potentially pick the spaghetti up? Are there other orders near the Italian restaurant, that we could co-schedule the spaghetti delivery with? In order to perform this matching of drivers and orders, DoorDash builds machine learning models that take into account historical data. In today’s episode, Raghav Ramesh explains how DoorDash’s data platform works, and how that data is used to build machine learning models. We also explore the machine learning model release process—which involves backtesting, shadowing, and gradual rollout. The post DoorDash Engineering with Raghav Ramesh appeared first on Software Engineering Daily.
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