Building an ML-powered delivery platform like DoorDash is a complex undertaking.
Tag Archives: ML
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
Increasing Operational Efficiency with Scalable Forecasting
Forecasting is essential for planning and operations at any business — especially those where success is heavily indexed on operational efficiency.
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.
How Artificial Intelligence Powers Logistics at DoorDash
In May, DoorDash participated at the O’Reilly Artificial Intelligence Conference in New York where I presented on “How DoorDash leverages AI in its logistics engine.” In this post, I walk you through the core logistics problem at DoorDash and describe how we use Artificial Intelligence (AI) in our logistics engine.