Introducing Fabricator: A Declarative Feature Engineering Framework
As machine learning (ML) becomes increasingly important across tech companies, feature engineering becomes a bigger focus for improving the predictive power of models. In fact, data scientists and ML engineers now spend 70% of their time on feature and data engineering. At DoorDash, most of our ML applications leverage our real-time prediction service (Sibyl), which is supported by a Redis- based
The Beginner’s Guide to Kotlin Coroutine Internals
When moving from a monolith to a microservices architecture, engineering teams often need to master a new programming paradigm. During our own migration, we opted to base our new backend architecture on Kotlin, shifting away from Python. Kotlin coroutines were an area our engineering teams needed to quickly understand. While Kotlin’s benefits include a rich ecosystem, interoperability with Java, a
Maintaining Machine Learning Model Accuracy Through Monitoring - DoorDash Engineering Blog
In the unit test approach, the data scientist opts in by analyzing the data, deciding the validations (such as, “I expect delivery time to be not more than one hour”), and recording these validations. These validations are then run on all new data. In the preceding example, the validation checks every delivery time input to be under one hour. However the introduction of new products can change thi
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How to Run Apache Airflow on Kubernetes at Scale
