SOTA ML Engineering / MLOps (2026)

Expert rules for building and auditing production machine-learning systems — the lifecycle that turns a model into a reliable, monitored, governed service. This is classical/predictive ML (tabular, ranking, vision, forecasting, recommendation): training pipelines, feature stores, model registries, serving, and drift monitoring. It is not LLM-application engineering — prompts, RAG, agents, and LLM evals live in sota-llm-engineering; data pipelines/warehouses live in sota-data-engineering. Grounded in Google's Rules of ML, the ML Test Score rubric, and Hidden Technical Debt in ML Systems. Every rule states the why; every rules file ends with an audit checklist.

Purpose

Two consumers, one source of truth: