Mlops

AI engineering as a discipline vs ML engineering; three-layer stack (application dev, model dev, infrastructure); product planning; last-mile challenge; use case taxonomy

Stateless retraining vs stateful training (fine-tuning); four stages of continual learning maturity; champion/challenger pattern; test in production methods (shadow deployment, A/B testing, canary release, interleaving experiments, bandits, contextual bandits); data freshness value; continuous ML challenges (feedback loops, emergency response, stable A/B baselines)

*Designing Machine Learning Systems* — Chip Huyen

When to use ML (nine conditions); research vs production differences (silent failure, latency vs throughput, messy data, fairness, interpretability); four system requirements (reliability, scalability, maintainability, adaptability); business vs ML objective alignment; problem framing (task types, decoupling objectives); mind vs data debate

Four-layer MLOps stack (storage/compute, resource management, ML platform, dev environment); cloud repatriation; Docker + Kubernetes; workflow orchestration tools (Airflow/Argo/Prefect/Metaflow); model store (8 artifact types); feature store (management/computation/consistency, eliminates training-serving skew); build vs buy framework

*Reliable Machine Learning* — Chen, Murphy, Parisa, Sculley, Underwood