Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth

apply()2021 - 30 minutes

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.


Hien Luu

Sr. Engineering Manager

DoorDash

Hien Luu is a Sr. Engineering Manager at DoorDash, leading the Machine Learning Platform team. He is particularly passionate about the intersection between Big Data and Artificial Intelligence. He is the author of the Beginning Apache Spark 2 book. Teaching is one his passions and he is currently teaching Apache Spark course at UCSC Silicon Valley Extension school. He has given presentations at various conferences like QCon SF, QCon London, Hadoop Summit, JavaOne, ArchSummit and Lucene/Solr Revolution.

Arbaz Khan

Machine Learning Platform Engineer

DoorDash

Arbaz is a Machine Learning Platform Engineer at DoorDash where he focuses on challenges around usability and scalability of online model serving. He has been directly involved in growing the scale of online model serving at DoorDash by more than 100x and helping multiple teams to productionize their ML business use cases. Previously, he had helped build machine learning platforms from the ground up for successful startups. Arbaz graduated from Indian Institute of Technology Kanpur (IIT-K) where he was awarded General Proficiency Medal for overall best academic performance in discipline of BTech-MTech dual degree in Computer Science.