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Adapted Switch-back Testing to Quantify Incrementality for App Marketplace Search Ads

Learn how DoorDash developed a switchback testing method to perform incrementality testing on an app marketplace ad platform

Kanhua Pan Yingying Chen Data Machine Learning

Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly

Data preparation, represents The vast majority of work in developing machine learning models, learn how to make things easier

Kornel Csernai Devjit Chakravarti Data

Building Scalable Real Time Event Processing with Kafka and Flink

Learn how DoorDash build a platform to process billions of events from different data sources, quickly, consistently and reliably

Allen Wang Data General

Building a Source of Truth for an Inventory with Disparate Data Sources

Learn how DoorDash crowdsources data from a variety of sources to help predict realtime inventory for our new connivence and grocery product

Anubhav Kushwaha Data

Using Back-Door Adjustment Causal Analysis to Measure Pre-Post Effects

When A/B testing is not recommended we can still quickly implement a new feature and measure its effects in a data-driven way.

Sharon Cui Data Machine Learning

Meet Dash-AB — The Statistics Engine of Experimentation at DoorDash

Learn how DoorDash was able to test uniformly according to established best practices and reuse complex statistical methods with Dash AB

Caixia Huang Yixin Tang Backend Data

How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

Learn about which caching libraries we considered, the analysis of our system and how we were able to use experiments to validate our approach.

Kornel Csernai Data

3 Principles for Building an ML Platform That Will Sustain Hypergrowth

Learn about the principles and big bets that enabled to scale and maintain our ML platform which supports our data users and data scientists

Hien Luu Data

Making Applications Compatible with Postgres Tables BigInt Update

Ensuring ID's don't get exceeded is an evolving challenge. Learn how DoorDash made our tables were compatible with our new Bigint upgrade

Maggie Fang Amiraj Dhawan 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 Data

How to Run Apache Airflow on Kubernetes at Scale

Learn how DoorDash managed to make its data orchestration more scalable and reliable with Kubernentes and Airflow

Akshat Nair Data

The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%

In a data driven world a company's experiment capacity directly impacts its development velocity. Learn what DoorDash did to boost testing 1000%

Sifeng Lin Yixin Tang

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