How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405 episode artwork

EPISODE · Aug 31, 2020 · 43 MIN

How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405

from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) · host Sam Charrington

Today we’re joined by Gary Ren, a machine learning engineer for the logistics team at DoorDash.  In our conversation, we explore how machine learning powers the entire logistics ecosystem. We discuss the stages of their “marketplace,” and how using ML for optimized route planning and matching affects consumers, dashers, and merchants. We also talk through how they use traditional mathematics, classical machine learning, potential use cases for reinforcement learning frameworks, and challenges to implementing these explorations.   The complete show notes for this episode can be found at twimlai.com/go/405! Check out our upcoming event at twimlai.com/twimlfest

Today we’re joined by Gary Ren, a machine learning engineer for the logistics team at DoorDash.  In our conversation, we explore how machine learning powers the entire logistics ecosystem. We discuss the stages of their “marketplace,” and how using ML for optimized route planning and matching affects consumers, dashers, and merchants. We also talk through how they use traditional mathematics, classical machine learning, potential use cases for reinforcement learning frameworks, and challenges to implementing these explorations.   The complete show notes for this episode can be found at twimlai.com/go/405! Check out our upcoming event at twimlai.com/twimlfest

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How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405

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This episode is 43 minutes long.

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This episode was published on August 31, 2020.

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Today we’re joined by Gary Ren, a machine learning engineer for the logistics team at DoorDash.  In our conversation, we explore how machine learning powers the entire logistics ecosystem. We discuss the stages of their “marketplace,” and how using...

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