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Machine Learning

Machine Learning

Using Gamma Distribution to Improve Long-Tail Event Predictions

Which loss function is best for long tail event prediction? learn how we used gamma distributions to boost eta prediction accuracy

Pratik Parekh Zhe Jia
Machine Learning

Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers

Epsilon-Greedy, The Upper Confidence Bound, Thompson Sampling: which is the best Multi-armed bandit algorithm for promotion optimization?

Arjun Sharma
Machine Learning

Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments

Learn how DoorDash balanced ML models' release speed and reliability by shipping darkly in order to manage fraud model deployments

Bob Nugman
Machine Learning

Balancing Network Effects, Learning Effects, and Power in Experiments

Experimenting can mean balancing learning and network effects, while ensuring adequate power. Learn how we manage this trilemna in this post

Wei Feng Jared Bauman
Data Machine Learning

Introducing Fabricator: A Declarative Feature Engineering Framework

Learn how our Fabricator infrastructure integrations for ML features were automated and continuously deployed to generate 500 unique features and 100+ billion daily feature values

Kunal Shah
Machine Learning

How DoorDash Quickly Spins Up Multiple Image Recognition Use Cases

Learn how DoorDash utilizes deep learning to power some of its image recognition use-cases

Chi Zhang Sushil Vellanki
Machine Learning

Improving Subgroup Analysis with Stein Shrinkage

Learn how DoorDash Utilized Stein Shrinkage to perform subgroup analysis without the danger of high variances

David Kastelman
Machine Learning

Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings

Learn how DoorDash used neural networks to better understand the contents of its large online catalog and improve search and recommendations

Abhi Ramachandran
Machine Learning

Increasing Operational Efficiency with Scalable Forecasting

Scaling forecasting to a large data team is not practical without a scalable platform. Learn how we built forecast factory at DoorDash

Ryan Schork
Machine Learning

Using ML and Optimization to Solve DoorDash’s Dispatch Problem

DoorDash delivers millions of orders every day with our last-mile logistics platform. Look under the hood and learn how the platform works

Alex Weinstein Jianzhe Luo
Machine Learning

Predicting Marketing Performance from Early Attribution Indicators

Learn how DoorDash managed to respond faster to changes in the market by building a model to utilize early attribution data from experiments

Zhe Mai
Machine Learning

Managing Supply and Demand Balance Through Machine Learning

Achieving DoorDash's objectives requires a good balance between the supply of Dashers and the demand for orders. Learn how we manage this with ML

Stas Sajin Zainab Danish

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