ML Systems, Production, and Engineering

ML Systems, Production, and Engineering covers: Designing Machine Learning Systems, Introducing MLOps, The ML Solutions Architect Handbook, Fundamentals of Data Engineering. Bridge the gap between "I can train a model in a notebook" and "I can design, deploy, and maintain ML systems in production." This is where your years of Rails engineering experience become a superpower. This is the ML equivalent of your SRE/observability knowledge. Chip Huyen's book is like your "Designing Data-Intensive Applications" but for ML. The concerns are identical to what you know from Rails production: how do you deploy safely, how do you monitor for regressions, how do you handle schema changes (data drift), how do you roll back a bad deploy (model rollback). Different vocabulary, same engineering discipline.