DoorDash

“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.”

“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…”

“Whereas in the past for an assignment algorithm, we might have looked back four weeks. We changed it to one week.”

“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.”

“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.”

“And this year we want to double, so to get to 50 plus people.”

“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 …”

“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.”

“It's mostly homegrown. We're using some technologies like Flint for one of some of our real time feature aggregation, data aggregation, but mostly it's homegrown.”

“We run a Databricks on S-III, but we pull data into Databricks from Snowflake, so we have a connection between the two.”

“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”

“I would say at all three places, there was a desire for a centralized stack so that iterative improvements sort of helped all models.”

“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.”