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.
Tag Archives: AI Infrastructure
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.
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
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.
Balancing Network Effects, Learning Effects, and Power in Experiments
At DoorDash, we rely on experimentation to make decisions regarding model improvements and product changes because we cannot perfectly predict the results in advance.
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.
How DoorDash Quickly Spins Up Multiple Image Recognition Use Cases
DoorDash has rich image data collected by Dashers, our delivery drivers, that we use in a number of use cases.
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.