Use this skill to review the ML lifecycle in Snowflake for reproducibility and governability: feature engineering and leakage, point-in-time correctness and training/serving skew, training reproducibility, the model registry and versioning, batch and continuous inference, drift and performance monitoring, ML lineage, and retraining and rollback policy. Trigger when a model is moving toward or already in production. Static review only: it never trains, registers, deploys, or invokes a model, and it never accepts an offline metric as production readiness.
