Machine Learning | DoorDash Engineering Blog

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General Machine Learning Best Practices for Regression-free Machine Learning Model Migrations

Migrating functionalities from a legacy system to a new service is a fairly common endeavor, but moving machine learning (ML) models is much more challenging. Successful migrations ensure that the new service works as well or better than its legacy version. But the complexity of ML models makes regressions more likely to happen and harder ...

Ying Chi

14 Minute Read

Data Machine Learning

Building Riviera: A Declarative Real-Time Feature Engineering Framework

In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...

Allen Wang

Kunal Shah 17 Minute Read

Machine Learning

Why Good Forecasts Treat Human Input as Part of the Model

At DoorDash, getting forecasting right is critical to the success of our logistics-driven business, but historical data alone isn’t enough to predict future demand. We need to ensure there are enough Dashers, our name for delivery drivers, in each market for timely order delivery. And even though it seems like people’s demand for food delivery ...

Brian Seo 10 Minute Read

Data Machine Learning

How to Drive Effective Data Science Communication with Cross-Functional Teams

Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...

James Williams

Lokesh Bisht 11 Minute Read

Data Machine Learning

Running Experiments with Google Adwords for Campaign Optimization

Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...

Yingying Chen

Heming Chen 16 Minute Read

Data Machine Learning

Building Flexible Ensemble ML Models with a Computational Graph

DoorDash extended its machine learning platform to support ensemble models.

Hebo Yang

Arbaz Khan

Param Reddy 14 Minute Read

Culture Machine Learning

Wanted: Data Scientists with Technical Brilliance AND Business Sense

DoorDash seeks data scientists who prioritize the business impacts of their work.

AlokGupta 13 Minute Read

Backend Culture Data General Machine Learning Mobile Web

2020 Hindsight: Building Reliability and Innovating at DoorDash

DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.

WayneCunningham 8 Minute Read

Machine Learning

Things Not Strings: Understanding Search Intent with Better Recall

For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results. We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search ...

Siddharth Kumar

Jimmy Zhou

Xiaochang Miao

Ashwin Kachhara 19 Minute Read

Machine Learning

Iterating Real-time Assignment Algorithms Through Experimentation

DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities. When customers place an order through DoorDash, they can expect it to be delivered within an hour. Our platform determines the Dasher, our term for a delivery driver, most suited to the task and offers the assignment. Complex real-time logistic ...

Sifeng Lin

Longsheng Sun 12 Minute Read

Data Machine Learning

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Arbaz Khan