As Artificial Intelligence (AI) systems move from prototypes to deployment, reliability and trustworthiness are increasingly limited by the data and AI pipeline lifecycle rather than by model training alone. Existing platforms offer strong support for individual lifecycle functions such as orchestration, experiment tracking, validation, or documentation, yet these capabilities are often adopted as separate tools, making provenance, observability, governance, and safe adaptation difficult to mana