As machine learning (ML) becomes increasingly important across tech companies, feature engineering becomes a bigger focus for improving the predictive power of models.
Category Archives: AI & ML
How DoorDash Quickly Spins Up Multiple Image Recognition Use Cases
DoorDash has rich image data collected by Dashers, our delivery drivers, that we use in a number of use cases.
Improving Subgroup Analysis with Stein Shrinkage
DoorDash is often interested in knowing not only the average effect of an intervention, but also the more granular effect of that intervention on specific cohorts.
Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings
Understanding the contents of a large digital catalog is a significant challenge for online businesses, but this challenge can be addressed using self-supervised neural network models.
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.
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.