How Lyft Is Improving Their Experiments Beyond A/B Testing

Lyft’s product manager John Kirn published an article recently about the challenges they face when conducting experiments. Existing experimentation techniques did not fully adapt to Lyft’s real-time business nature or mitigate network effects. Lyft’s Experimentation team deployed new ones, such as time and region split testing, and improved internal experimentation norms and techniques. The well-known technique known as A/B …

DoorDash Introduces Dash-AB: A Centralized Library For Statistical Analysis

Every change in a data-driven firm must be tested through trials to guarantee that it has a positive, measurable impact on key performance measures. Every month, thousands of trials are running in parallel at DoorDash. To guarantee that this volume of testing is possible, the results of each experiment must be assessed swiftly and precisely. …

DoorDash Introduces ‘Fabricator’: A Centralized Framework For Feature Engineering To Improve Tthe Predictive Power of Machine Learning Models

Feature engineering is becoming a significant priority for boosting the predictive capacity of models as machine learning (ML) becomes more essential across digital businesses. Indeed, data scientists and machine learning engineers currently devote 70% of their time to feature and data engineering. Most of DoorDash’s machine learning applications use Sibyl, the real-time prediction engine powered …

How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eats

TweetShareShareVoteReddit In Uber’s ride-hailing business, a driver picks up a user from a curbside or other location, and then drops them off at their destination, completing a trip. Uber Eats, our food delivery service, faces a more complex trip model. When a user requests a food order in the app, the specified restaurant begins preparing …