How do you build reliable training pipelines that are reproducible?

Reliable training pipelines are deterministic and versioned. Key practices: - Version data snapshots and labels - Version code + config - Track features and preprocessing - Log metrics and artifacts Use pipeline orchestration (Airflow/Kubeflow) and ensure the same transformations run in training and inference to avoid training-serving skew.