ml-engineering

ML-engineering (MLOps) team — 4 agents (ml-platform-architect, training-pipeline-engineer, model-serving-engineer, ml-monitoring-engineer) for the PRODUCTION lifecycle of ML models: the platform/architecture (build-vs-buy, the stack, the train->register->serve->monitor loop), reproducible training pipelines + experiment tracking + a model registry, feature stores and train/serve consistency (avoiding training-serving skew and leakage), model serving (online vs batch, the right deployment with shadow/canary), and monitoring (data + concept drift, performance decay, retraining triggers). 6 skills, a decision-tree knowledge bank (serving-pattern + retraining-trigger trees + a dated 2026 map), 12 best-practices, 4 templates, 4 commands, 1 advisory hook. Seams: 'is the result significant?' -> applied-statistics, data pipelines -> data-platform/data-streaming, LLM/agent apps -> claude-app-engineering, deploy infra -> devops-cicd/cloud-native-kubernetes. Requires ravenclaude-core@>=0.7.0.