apply() - Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth

Wednesday, April 21, 2:05pm - 2:35pm (EDT)

Time shown in-04:00 America, New York

Speakers: Hien Luu and Arbaz Khan, DoorDash

As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and the ranking of the large volume of search queries.

The prediction service built to meet these use cases now supports many dozens of models spanning different Machine Learning algorithms such as gradient boosting, neural networks and rule-based. The service supports greater than 10 billion predictions every day with a peak hit rate of above 1 million per second.

In this session, we will share our journey of building and scaling the prediction service, the various optimizations experimented, lessons learned, technical decisions and tradeoffs made. We will also share how we measure success and how we set goals for the future. Finally, we will end by highlighting the challenges ahead of us in extending the service to wider use cases across the DoorDash machine learning realm.

Add to Calendar 2021/04/21 11:05:00 2021/04/21 11:35:00 America/Los_Angeles apply() - Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth Speakers: Hien Luu and Arbaz Khan, DoorDash

As DoorDash business grows, the online ML prediction volume grows exponentially to support the various Machine Learning use cases, such as the ETA predictions, the Dasher assignments, the personalized restaurants and menu items recommendations, and the ranking of the large volume of search queries.

The prediction service built to meet these use cases now supports many dozens of models spanning different Machine Learning algorithms such as gradient boosting, neural networks and rule-based. The service supports greater than 10 billion predictions every day with a peak hit rate of above 1 million per second.

In this session, we will share our journey of building and scaling the prediction service, the various optimizations experimented, lessons learned, technical decisions and tradeoffs made. We will also share how we measure success and how we set goals for the future. Finally, we will end by highlighting the challenges ahead of us in extending the service to wider use cases across the DoorDash machine learning realm. https://us02web.zoom.us/j/81406729277 or YouTube Live link on Slack false MM/DD/YYYY 30 OPAQUE awFyHhintzGWPNfvXmVh109699

https://us02web.zoom.us/j/81406729277 or YouTube Live link on Slack

Link: