This paper concerns the lifecycle and runtime management of AI components of AI-native networks, in which the AI components are deeply integrated with network functions. The paper presents an overview of the standardised, IT-based, and research approaches to ML pipeline management. To address the identified gaps, we introduce the automated MLOps as a Service concept in which an ML model deployed in the network is continuously monitored for data and concept drifts, as well as network performance degradation, and on that basis, specific ML pipeline operations are triggered to improve the model's operation. The proposed approach allows for selecting local (inplace) or remote training by a network operator or external ML service providers. The benefits of selecting different training options are demonstrated in the AI-based network analytics scenario.

The paper consists of a description of 5G-UASP, a multi-provider platform for drone services. It takes into account the involvement of air traffic management authority in all flight phases. We describe the core components of 5G-UASP and outline the procedures related to flight preparation, execution and termination phases. Instantiation of the concept is presented for 5G network. As it is shown, the usage of network slicing and MEC enables an immersive implementation of the architecture. Two options of instantiation of the reference architecture are included.