Production model deployment

This document discusses the challenges of deploying machine learning models. It describes the model lifecycle from data to deployment and identifies three common modes of deployment. Key challenges discussed include ensuring models continue to accurately predict outcomes over time as data changes, meeting engineering requirements for throughput and latency, and establishing team structures that support continuous model development and deployment through open standards and capabilities. Solutions proposed focus on continuous training, materializing model outputs, and organizing multi-disciplinary teams around model capabilities rather than functions.