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Spotlight on Data: How DoorDash Solved the Global Optimality Problem with Raghav Ramesh

An interactive case study from DoorDash

Raghav Ramesh

The common algorithmic challenge for the current crop of marketplace companies is efficient matching between two sides of the marketplace. For DoorDash, which focuses on food delivery, this problem is even more difficult because of its three-sided marketplace, where the company must identify the optimal Dasher to fulfill a delivery from a restaurant and bring it to a consumer.

At its core, this marketplace logistics problem is a core operations research problem called the vehicle routing problem. But DoorDash faces additional challenges: delivery requests come in in real time; most orders need to be delivered immediately; Dashers are in constant movement; and the effects of variance in restaurant operations and real-world events (traffic, weather, etc.) have pronounced effects on the solutions. Thus, finding global optimality in real time becomes intractable. To address these challenges, DoorDash leverages various AI techniques to intelligently model the decision space and achieve near-optimal solutions in seconds.

In this edition of Spotlight on Data, DoorDash’s Raghav Ramesh explains how his team defined the problem of modeling the marketplace and then implemented machine learning techniques to solve the marketplace matching problem. Join us to find out how AI can help with similarly complex optimization problems.

O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions for the future.

In every edition of Spotlight on Data, you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.

What you'll learn-and how you can apply it

By the end of this live show, you’ll better understand:

  • How DoorDash processes and optimizes customer requests in real time at a global scale
  • How to use machine learning techniques to solve complex optimization problems

This training course is for you because...

  • You're a data scientist, engineer, or developer building ML systems to address large-scale optimization problems.

Prerequisites

  • Come with your questions for Raghav Ramesh
  • Have a pen and paper handy to capture notes, insights, and inspiration

Recommended follow-up:

About your instructor

  • Raghav Ramesh is the lead machine learning engineer at DoorDash working on the core logistics engine, where he focuses on AI problems including vehicle routing, real-time predictions for delivery assignments, demand forecasting, and marketplace balancing and pricing. Previously, he worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics. Raghav holds a master’s degree from Stanford University, where he focused on artificial intelligence and operations research.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Monday, October 21, 2019, at 10:00am PT / 1:00pm ET

  • Introduction and presentation (15 minutes)
  • Interactive discussion and Q&A (45 minutes)