Systematic Approaches and Creativity; Building DoorDash's ML Platform During the Pandemic
Speakers Bio:
Hien Luu, Senior Engineering Manager at 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.
Dawn Lu, Senior Data Scientist at DoorDash
Dawn Lu is a senior data scientist at DoorDash, a technology company that empowers merchants to grow their businesses by offering on-demand delivery. She focuses primarily on developing and deploying machine learning models that power DoorDash's logistics engine, including food ready time predictions, supply & demand forecasting, as well as pay algorithms. Before joining the technology industry, she worked as a strategy consultant — and during this time she discovered her passion for using quantitative approaches to better understand and predict consumer behavior. She holds a Bachelor's degree in Economics from Yale University.
Abstract:
What is it like to build an ML platform during the pandemic, with a new team, at a new company?
DoorDash’s mission is to grow and empower local economies. As DoorDash's business grows, it is essential to establish a centralized ML platform to accelerate the ML development process and to power the numerous ML use cases.
This presentation will detail the DoorDash ML platform journey during the pandemic, which includes the way we establish a close collaboration and relationship with the Data Science community, how we intentionally set the guardrails in the early days to enable us to make progress, the principled approach of building out the ML platform while meeting the needs of the Data Science community, and finally the technology stack and architecture that powers billions of predictions per day and supports a diverse set of ML use cases.