The Ledger
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DoorDash aims to offer white-label infrastructure services modeled after AWS
“I think we would like to think of ourselves as an infrastructure company. That's right. Where we build products and services to help us in the things we're trying to do, but ultimately we get them to a stage of maturity and robustness through experimenting on …”
Problem framing is the hardest part of the machine learning lifecycle
“I think right at the start of a project is probably, and probably the hardest and most impactful part of the life cycle of the model. I think I prefer to call it sort of data-driven software because it could ultimately be it could be something other than a mod…”
DoorDash commits raw training scripts instead of pickled machine learning models
“Instead of building a model and pickling it or whatever a conversion package you want to use, we commit the actual training script to our model library.”
Deploying ML models to production at DoorDash requires data science approval
“It is perfectly acceptable for anyone at DoorDash to Build a machine learning model, but if they want to put it into production, they need approval or a review from someone like a data scientist or machine learning engineer.”
Training dedicated models for distinct cohorts beats fitting general models
“If there's a strong enough use case for a different cohort or segment like new users, typically it'll make more sense just to train a new model rather than trying to shoehorn it into a general model.”
Grocery expansion turns DoorDash from a three- to four-sided marketplace
“The food delivery is a three-sided. You're right. There's the merchant, the consumer, and the dasher. As we start to branch out into grocery delivery, convenience delivery, it becomes a four-sided where we introduce a picker as well.”
DoorDash standardized its core machine learning platform on LightGBM and PyTorch
“We landed on using a framework that enables tree-based models. And we picked light GBM for that after trying a few different packages and also deep learning. And for that, we then used PyTorch. And so we started with those two core libraries.”
DoorDash, Airbnb, and Lyft all sought a centralized machine learning stack
“I would say at all three places, there was a desire for a centralized stack so that iterative improvements sort of helped all models.”
Consolidating training and serving data is the feature store holy grail
“And so a way to create a feature store whereby we can consolidate the training data with the prediction serving time data is a common sort of holy grail for all the companies I've been at in their pursuit.”
DoorDash's data science team grew from six to thirty in 18 months
“We, so a year and a half ago when I joined we had six, five or six people on the team. We're now at almost 30 people a year and a half later”
DoorDash plans to double its data science team in 2021
“And this year we want to double, so to get to 50 plus people.”
DoorDash shortened ML model lookback from four weeks to one during COVID
“Whereas in the past for an assignment algorithm, we might have looked back four weeks. We changed it to one week.”