Migrating functionalities from a legacy system to a new service is a fairly common endeavor, but moving machine learning (ML) models is much more challenging. Successful migrations ensure that the new service works as well or better than its legacy version. But the complexity of ML models makes regressions more likely to happen and harder ...
Learn how we migrated our pricing logic to microservices
Given the importance of time in our services and the need to scale, java.time works much better than primitives.
DoorDash's decision engine empowers customer service agents to deliver consistent, effective solutions for customer issues.
DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.
In 2019, DoorDash’s engineering organization initiated a process to completely reengineer the platform on which our delivery logistics business is based. This article represents the first in a series on the DoorDash Engineering Blog recounting how we approached this process and the challenges we faced. In traditional web application development, engineers write code, compile it, ...
DoorDash opens a new tech office in Seattle to support its Drive and DashMart business lines.
Lessons for developing a fast, flexible, and scalable map feature on web
From a product engineering perspective, external partnerships can be tricky. Here are four best practices to follow.
Learn how we built a discrete event simulator for location data tests
Ensure your growing team can search, analyze, and visualize data securely by integrating Splunk with a custom built Terraform provider.
Learn how the DoorDash merchant team has scaled up to support merchants during the Covid-19 crisis
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