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.
Category Archives: AI & ML
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.
Meet Dash-AB: The Statistics Engine of Experimentation at DoorDash
For any data-driven company, it’s key that every change is tested by experiments to ensure that it has a positive measurable impact on the key performance metrics.
Building the Model Behind DoorDash’s Expansive Merchant Selection
An optimized merchant selection strategy has been one of the key factors that has enabled DoorDash to become an industry leader in US food delivery service.
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers
Maintaining Dasher supply to meet consumer demand is one of the most important problems for DoorDash to resolve in order to offer timely deliveries.
Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments
At DoorDash, machine learning (ML) models are invoked many millions of times each day.
Balancing Network Effects, Learning Effects, and Power in Experiments
At DoorDash, we rely on experimentation to make decisions regarding model improvements and product changes because we cannot perfectly predict the results in advance.