D. Sculley

Books in this wiki: Reliable Machine Learning

Background

Research scientist and ML systems practitioner at Google. Co-author of the influential paper "Hidden Technical Debt in Machine Learning Systems" (NeurIPS 2015) and co-author of Reliable Machine Learning (O'Reilly, 2022).

Core Positions

ML systems accumulate technical debt in ways fundamentally different from conventional software — particularly around data dependencies, feedback loops, and the entanglement of data and code. The "hidden technical debt" framing has become a standard way to discuss ML system complexity.