Writing
Notes on Machine Learning, Research and Production
Essays on the technical problems worth thinking about, because good ideas get better under scrutiny.
Series
ML in Research & Production
Machine Learning (ML) in Research and Production
Opens the series by unpacking why ML models behave differently once they leave the research environment, examining assumptions that hold in academic settings but break down under real-world production constraints.
Read on MediumMachine Learning (ML) in Research and Production: Data Requirements
Digs into how data requirements shift between research and production ML, covering volume, quality and latency considerations researchers rarely confront until deployment.
Read on MediumUnderstanding Data in Production Machine Learning Systems: A Visual Guide for Practitioners
Closes the series with a visual breakdown of how data drift, validation and monitoring shape the day-to-day reliability of production ML systems, aimed at practitioners building and maintaining live pipelines.
Read on MediumEssays
Standalone Reflections
Let's Build Something That Matters
If you have a decision that depends on predicting risk in a complex system, I'd like to hear about it.