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Experiment Faster and with Less Effort

Business Policy Experiments Using Fractional Factorial Designs

At DoorDash, we constantly strive to improve our experimentation processes by addressing four key dimensions, including velocity to increase how many experiments we can conduct,  toil to minimize our launch and analysis efforts, rigor to ensure a sound experimental design and robustly efficient analyses, and efficiency to reduce costs associated with our experimentation efforts.

Using CockroachDB to Reduce Feature Store Costs by 75%

While building a feature store to handle the massive growth of our machine-learning (“ML”) platform, we learned that using a mix of different databases can yield significant gains in efficiency and operational simplicity.

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?