Forecasting is essential for planning and operations at any business — especially those where success is heavily indexed on operational efficiency.
Category Archives: AI & ML
Using ML and Optimization to Solve DoorDash’s Dispatch Problem
DoorDash delivers millions of orders every day with the help of DeepRed, the system at the center of our last-mile logistics platform.
Predicting Marketing Performance from Early Attribution Indicators
DoorDash uses machine learning to determine where best to spend its advertising dollars, but a rapidly changing market combined with frequent delays in data collection hampered our optimization efforts.
Managing Supply and Demand Balance Through Machine Learning
At DoorDash, we want our service to be a daily convenience offering timely deliveries and consistent pricing.
Maintaining Machine Learning Model Accuracy Through Monitoring
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.
Improving ETA Prediction Accuracy for Long-tail Events
Long-tail events are often problematic for businesses because they occur somewhat frequently but are difficult to predict.
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.
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.
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.
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.