Scaling the Machine Learning Platform at DoorDash

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DoorDash’s mission is to grow and empower local economies. DoorDash’s business is a 3-sided marketplace composed of Dashers, consumers, and merchants.

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.  We are making good progress, but we are still in the early days of building out our ML platform.

This presentation will detail the DoorDash ML platform journey that 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. They include search ranking, recommendation, fraud detection, food delivery assignment, food delivery arrival time prediction, and more.

Q&A

Question: Have you considered a graph DB for correlating features? (e.g. rather than a k/v)?

Answer: yes, we are considering using Neo4j for a bigger upcoming project.

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Question: How did you make sure the ML Platform you were building would solve most of the pain points for the customers (data scientists, ML engineers etc). How did you go back collecting that feedback. Was it through the ML council as well?

Answer: We have many ways to interact w/ DSs, via slack, regular training sessions, etc.

Question: Tying into the first question, sometimes the data scientists might be nervous for the uptake of a new process/platform. How did you go about onboarding customers onto the platform.

Answer: Whenever there is a new use case, we collaborate pretty closely with them from the beginning of their project. Documentation plays an important part in onboarding customers

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Question: Bit of a silly and fun question, but kind of interesting in terms of service quality and machine learning I suppose: I always have problems with my pizza ordering cold when I use delivery apps - Doordash as well as Ubereats and Deliveroo. I've noticed this can be due to a number of reasons:

1. The restaurant is far away and there's traffic

2. The driver picks a slower route

3. The driver arrives late at the restaurant

4. The driver is delivering an order on the way

5. The driver is delivering an order after mine but had to wait for the next order to be completed at the restaurant

Do you know of any strategies for what restaurant to choose to ensure my pizza arrives hot? Rating, distance, photo quality or other? Are there any good predictors of pizza arrival hotness in the application UI?

Also, I think you briefly mentioned food arrival quality in your talk. What kind of predictions are in place to ensure this, and what sort of models are you looking to train (or features to train on) to improve this in the future?

Answer: There are two parts - food order pickup time and food delivery time. These are both difficult problems because we don't control the weather, traffic, how busy the restaurants are. We have some control about when to ask the dashers to pickup the food. The logistics team is constantly looking for ways to improve their models to reduce the pickup time and delivery time.

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Question: Was wondering if Doordash allows it’s merchants to give their input into the weightings for the recommendations model? This could get rid of issues such as recommending unrelated sides/drinks in orders, like dumplings as sides for dumplings

Answer: Good question. I am not aware of this at the moment. Maybe we should figure out a way for customers to give feedback on the bad recommendations.

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