Centralized MLOps Platform for Software Defined Vehicles
Abstract
Software complexity in automotive has increased fourfold since 2010, yet productivity has remained flat. This growing gap threatens automakers’ ability to innovate while spending on automotive software continues to climb, over $100billion in recent years, with projections to double every 7 to 8 years. The shift to software defined Vehicles addresses this through hardware consolidation replacing dozens of distributed ECUs with centralized computers that enable software virtualization and hardware to software decoupling. This transformation brings new challenges such as managing ML models across fragmented paltforms, diverse hardware configuration, limited edge computing resources, intermittent connectivity, and strict safety requirements under ISO 26262 and SOTIF. Traditional MLOps practices don’t work in automotive contexts. This paper presents a centralized MLOps platform designed specifically for software defined vehicles, handling the complete ML lifecycle from development to deployment, monitoring and continuous improvement. The platform uses containerization, model versioning, automated validation, edge optimized inference to manage complexity while at the same time delivering the operational excellence required by Software Defined Vehicles.