Engineering Best Practices for Machine Learning
SE-ML is a research initiative cataloging engineering best practices for machine learning systems. Their framework covers 46 practices across 6 categories: Data, Training, Coding, Deployment, Team, and Governance -- providing actionable guidance for building production-quality ML systems.
Operating-system patterns and coordination intelligence will appear here as SE-ML publishes them. SE-ML is set up and active on OTP.