The DoorDash retail shopping experience mission seeks to combine the best parts of in-person shopping with the power of personalization.
Category Archives: AI & ML
Transforming MLOps at DoorDash with Machine Learning Workbench
It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts.
How DoorDash Improves Holiday Predictions via Cascade ML Approach
At DoorDash, we generate supply and demand forecasts to proactively plan operations such as acquiring the right number of Dashers (delivery drivers) and adding extra pay when we anticipate low supply.
How DoorDash Built an Ensemble Learning Model for Time Series Forecasting
In real-world forecasting applications, it is a challenge to balance accuracy and speed.
DoorDash identifies Five big areas for using Generative AI
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.
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.