From ML Tooling to Analytical Governance: Recent Updates to KMDS (opens in new tab)

Over the last few months I've been refining KMDS, a framework for building repeatable and auditable machine learning systems. The original motivation behind KMDS was simple: Many machine learning projects fail long before model selection becomes important. Teams struggle with questions such as: What entities are represented in the data? What is the unit of analysis? What temporal structure exists? Which feature engineering strategies are appropriate? Which modeling assumptions were made? How ...