Machine Learning
Solving for Unobserved Data in a Regression Model Using a Simple Data Adjustment
Learn how our team optimized prep time estimates while overcoming censored data
JH
Sri Santhosh
Hari
Data Machine Learning
Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity
To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X
Jessica
Zhang
Yixin
Tang
Data Machine Learning
How DoorDash is Scaling its Data Platform to Delight Customers and Meet our Growing Demand
Learn the challenges and best practices to successfully growing a data platform organization
Sudhir
Tonse
Machine Learning
Leveraging Causal Modeling to Get More Value from Flat Experiment Results
Using Heterogeneous Treatment Effects to improve personalization
Mitchell
Koch
Jared
Bauman
Machine Learning
Retraining Machine Learning Models in the Wake of COVID-19
When the COVID-19 pandemic significantly changed how people took their meal, DoorDash had to retrain demand prediction machine learning models.
Austin
Cai
Data Machine Learning
Supporting Rapid Product Iteration with an Experimentation Analysis Platform
DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.
Arun
Balasubramani
Machine Learning
Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging
Learn how we built a classification model quickly, cheaply, and at scale
Abhi
Ramachandran
Machine Learning
Optimizing DoorDash’s Marketing Spend with Machine Learning
Learn how we utilized cost curves to automate our marketing campaigns at scale
Aman
Dhesi
Backend Data Machine Learning
Enabling Efficient Machine Learning Model Serving by Minimizing Network Overheads with gRPC
Learn the challenges of reducing network overheads with gRPC optimizations
ArbazKhan
Data Machine Learning
Meet Sibyl – DoorDash’s New Prediction Service – Learn about its Ideation, Implementation and Rollout
Learn how building a prediction service enables the utilization of ML models based on real-time data
Cody
Zeng
Machine Learning
Improving Experimental Power through Control Using Predictions as Covariate (CUPAC)
Too much varience can reducing experimental power. Learn how we solved this problem with our new CUPAC method
Jeff
Li
Yixin
Tang
Jared
Bauman
Machine Learning
DoorDash’s ML Platform – The Beginning
Learn how we increased the scalability and productivity of the data science team by building a machine learning platform
Param
Reddy