DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.
Category Archives: Data
The Undervalued Skills Candidates Need to Succeed in Data Science Interviews
After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances .
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.
Uncovering Online Delivery Menu Best Practices with Machine Learning
Restaurants on busy thoroughfares can use many elements to catch a customer’s eye, but online ordering experiences mostly rely on the menu to generate sales.
Hot Swapping Production Tables for Safe Database Backfills
Making changes to data tables can disrupt production systems that need to be operating 24/7. DoorDash engineering explains how to hot swap tables so as not to interfere with production systems.
Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity
Data-driven companies measure real customer reactions to determine the efficacy of new product features, but the inability to run these experiments simultaneously and on mutually exclusive groups significantly slows down development.
Integrating a Search Ranking Model into a Prediction Service
As companies utilize data to improve their user experiences and operations, it becomes increasingly important that the infrastructure supporting the creation and maintenance of machine learning models is scalable and will enable high productivity.
How DoorDash is Scaling its Data Platform to Delight Customers and Meet our Growing Demand
Today, many of the fastest growing, most successful companies are data-driven.
Supporting Rapid Product Iteration with an Experimentation Analysis Platform
DoorDash’s new experimentation platform, built on a combination of SQL, Kubernetes, and Python, allows for quick iteration of data-driven feature improvements.
Enabling Efficient Machine Learning Model Serving by Minimizing Network Overheads with gRPC
The challenge of building machine learning (ML)-powered applications is running inferences on large volumes of data and returning a prediction over the network within milliseconds, which can’t be done without minimizing network overheads.