Machine Learning

Featured Article

Data Machine Learning Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Read More

Arbaz Khan

21 Minute Read

Data Machine Learning

Uncovering Online Delivery Menu Best Practices with Machine Learning

Learn how we analyzed over 100K online delivery menus to develop menu best practices

Read More Finn Qiao

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

Read More Joe Harkman

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

Read More Jessica Zhang

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

Read More Sudhir Tonse

Machine Learning

Leveraging Causal Modeling to Get More Value from Flat Experiment Results

Using Heterogeneous Treatment Effects to improve personalization

Read More Mitchell Koch

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.

Read More 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.

Read More 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

Read More 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

Read More 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

Read More Arbaz Khan

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

Read More 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

Read More Jeff Li

Thank you for subscribing!