A Distributed ML Framework for Service Deployment in the 5G-based Automotive Vertical

5G is the convergence technology for the new generation of mobile networks, expected to be massively deployed in the coming years. Building on network slicing and edge computing capabilities, 5G promises to address the diverse and quite demanding performance requirements of a wide range of use cases (UCs). As a result of these technological transformations, vertical industries will have enhanced technical capacity available to trigger the development of new products and services. Driven by these advances, Machine learning (ML) applications are headed towards collaborative distributed (CDL) schemes, to exploit the abundance of clients’ data. Contrary to the traditional cloud-based centralized solutions (CML), in CDL schemes, computational load is shifted to the intelligent edge and extends further beyond, to the user-equipment (including connected vehicles). Here, we present a distributed ML (DML) framework, that will provide functionalities for simplified management and orchestration of collections of ML service components and will allow ML-based applications to penetrate the Automotive world.