When and how to update AI/ML models in 6G resource-constrained network domains?

Artificial intelligence and Machine Learning (AI/ML) are steadily becoming widespread across all layers in the current mobile network generation. The next generation of networks, namely 6G, will consider AI/ML as a foundational block to deliver all expected results of such networks. Thus, the management of AI/ML applications across different layers of 6G is paramount for the success of the next generation of mobile networks. However, current approaches to the management of AI/ML, namely Machine Learning Operations (MLOps), focus mostly on independent AI/ML operations for a specific problem with the goal of improving the performance of the overall models of the AI/ML application. While this approach is useful for scenarios where resources are plentiful, such as the cloud layer, in resource-constrained network domains, focusing only on performance is not the best approach. For example, in the network edge domain, resources such as energy and computation are limited; thus, when and how to update an AI/ML model is a critical question to answer. In this paper, we focus on MLOps in scenarios with limited resources. To this end, we propose a network component for 6G, namely the Model Manager. This component automatically decides when and how to update a given AI/ML model based on both performance and resource consumption points of view. For this, we introduce a Model Manager model score to decide the approach to updating an AI/ML model. Our experiments show that by using this score, the model manager could find suitable situations on how to update a model without manual configuration.