In the wake of ChatGPT and Generative AI DoorDash is identifying ways this new technology can enhance the customer’s ordering experience on the platform.
Category Archives: AI & ML
Lifecycle of a Successful ML Product: Reducing Dasher Wait Times
Building an ML-powered delivery platform like DoorDash is a complex undertaking.
How DoorDash Upgraded a Heuristic with ML to Save Thousands of Canceled Orders
One challenge in running our platform is being able to accurately track Merchants’ operational status and ability to receive and fulfill orders.
Selecting the Best Image for Each Merchant Using Exploration and Machine Learning
In order to inspire DoorDash consumers to order from the platform there are few tools more powerful than a compelling image, which raises the questions: what is the best image to show each customer, and how can we build a model to determine that programmatically using each merchant’s available images?
Augmenting Fuzzy Matching with Human Review to Maximize Precision and Recall
Even state-of-the-art classifiers cannot achieve 100% precision.
Homepage Recommendation with Exploitation and Exploration
Building quality recommendations and personalizations requires delicately balancing what is already known about users while recommending new things that they might like.
Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly
The vast majority of work in developing machine learning models in the industry is data preparation, but current methods require a lot of intensive and repetitive work by practitioners.
Evolving DoorDash’s Substitution Recommendations Algorithm
When expanding from made-to-order food delivery to new product verticals like groceries, convenience, and retail, new challenges arise, including how to ensure inventory will be available to fulfill orders.
4 Essential Steps for Building a Simulator
For complex systems such as the DoorDash assignment system, simulating the impact of algorithmic changes is often faster and less costly than experimenting on features live in production.
Leveraging Causal Inference to Generate Accurate Forecasts
For any operations-intensive business, accurate forecasting is essential but is made more difficult by hard-to-measure factors that can disrupt the normal flow of business.