Ejecutar miles de experimentos con eficacia significa equilibrar cuidadosamente nuestra velocidad con los controles necesarios para mantener la confianza en los resultados experimentales, pero encontrar ese equilibrio nunca es fácil.
Category Archives: Data
Pruebas Switch-back adaptadas para cuantificar la incrementalidad de los anuncios de búsqueda en App Marketplace
At DoorDash, we use experimentation as one of the robust approaches to validate the incremental return on the marketing investment.
Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly
The vast majority of work in developing machine learning models in the industry is data preparation, but current methods require a lot of intensive and repetitive work by practitioners.
Building Scalable Real Time Event Processing with Kafka and Flink
At DoorDash, real time events are an important data source to gain insight into our business but building a system capable of handling billions of real time events is challenging.
Creación de una fuente de veracidad para un inventario con fuentes de datos dispares
Managing inventory becomes a serious challenge when transitioning from food delivery — where the item ordered is prepared on demand — to grocery and alcohol delivery.
Using Back-Door Adjustment Causal Analysis to Measure Pre-Post Effects
When A/B testing is not recommended because of regulatory requirements or technical limitations to setting up a controlled experiment, we can still quickly implement a new feature and measure its effects in a data-driven way.
Meet Dash-AB — The Statistics Engine of Experimentation at DoorDash
For any data-driven company, it’s key that every change is tested by experiments to ensure that it has a positive measurable impact on the key performance metrics.
How We Applied Client-Side Caching to Improve Feature Store Performance by 70%
At DoorDash, we make millions of predictions every second to power machine learning applications to enhance our search, recommendation, logistics, and fraud areas, and scaling these complex systems along with our feature store is continually a challenge.
3 Principles for Building an ML Platform That Will Sustain Hypergrowth
Taking full advantage of a large and diverse set of machine learning (ML) use cases calls for creating a centralized platform that can support new business initiatives, improve user experiences, enhance operational efficiency, and accelerate overall ML adoption.
Compatibilización de aplicaciones con tablas Postgres Actualización de BigInt
Anteriormente, DoorDash confiaba en Postgres como su principal almacenamiento de datos y utilizaba modelos de base de datos Python Django para definir los datos.