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 models 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.
May 26, 2021 11:30 AM PT
DoorDash is a 3-sided marketplace that consists of Merchants, Consumers, and Dashers.
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 above 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 our Machine Learning platform and particularly 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 our Machine Learning platform to support the Data Scientist community and a wider set use cases at DoorDash.