Making accurate predictions when historical information isn’t fully observable is a central problem in delivery logistics.
Category Archives: AI & ML
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.
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.
Leveraging Causal Modeling to Get More Value from Flat Experiment Results
A/B tests and multivariate experiments provide a principled way of analyzing whether a product change improves business metrics.
Retraining Machine Learning Models in the Wake of COVID-19
The advent of the COVID-19 pandemic created significant changes in how people took their meals, causing greater demand for food deliveries.
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.
Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging
Companies with large digital catalogs often have lots of free text data about their items, but very few actual labels, making it difficult to analyze the data and develop new features.
Building a system that can support machine learning (ML)-powered search and discovery features while simultaneously being interpretable enough for business users to develop curated experiences is difficult.
Optimizing DoorDash’s Marketing Spend with Machine Learning
Over a hundred years ago, John Wanamaker famously said “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half”.
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.
Meet Sibyl РDoorDash’s New Prediction Service РLearn about its Ideation, Implementation and Rollout
As companies utilize data to optimize and personalize customer experiences, it becomes increasingly important to implement services that can run machine learning models on massive amounts of data to quickly generate large-scale predictions.