It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts.
Category Archives: ML Infrastructure
How DoorDash Improves Holiday Predictions via Cascade ML Approach
At DoorDash, we generate supply and demand forecasts to proactively plan operations such as acquiring the right number of Dashers (delivery drivers) and adding extra pay when we anticipate low supply.
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.
4 Essential Steps for Building a Simulator
For complex systems such as the DoorDash assignment system, simulating the impact of algorithmic changes is often faster and less costly than experimenting on features live in production.
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.
Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments
At DoorDash, machine learning (ML) models are invoked many millions of times each day.
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.
Improving Subgroup Analysis with Stein Shrinkage
DoorDash is often interested in knowing not only the average effect of an intervention, but also the more granular effect of that intervention on specific cohorts.
Maintaining Machine Learning Model Accuracy Through Monitoring
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.
Best Practices for Regression-free Machine Learning Model Migrations
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.