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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

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

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

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
Backend Data

Building Faster Indexing with Apache Kafka and Elasticsearch

DoorDash describes how it built a faster search index using open source projects.

Satish Saley Danial Asif Siddharth Kumar

Overcoming Rapid Growth Challenges for Datasets in Snowflake

To meet additional SLA's of DoorDash's rapidly growing team without increasing compute we had to rely on a variety of ETL optimizations.

Andrew Huynh Ashwini Manjunath
Data Machine Learning

Maintaining Machine Learning Model Accuracy Through Monitoring

Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.

Swaroop Chitlur Kornel Csernai
Data Machine Learning

Building Riviera: A Declarative Real-Time Feature Engineering Framework

In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...

Allen Wang Kunal Shah
Data Machine Learning

How to Drive Effective Data Science Communication with Cross-Functional Teams

Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...

James Williams Lokesh Bisht
Data Machine Learning

Running Experiments with Google Adwords for Campaign Optimization

Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...

Yingying Chen Heming Chen
Data Machine Learning

Building Flexible Ensemble ML Models with a Computational Graph

DoorDash extended its machine learning platform to support ensemble models.

Hebo Yang Arbaz Khan Param Reddy

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