Advancing AI-Native 6G Networks by AI Enablers

The evolution toward AI-native networks necessitates seamless Artificial Intelligence (AI) integration, which demands privacy-preserving data management, scalable model deployment, and optimized resource allocation. To address these challenges, we introduce key AI enablers as Data Operations (DataOps), Machine Learning Operations (MLOps), and AI as a Service (AIaaS). DataOps enables secure, privacy-preserving data collection through techniques such as Differential Privacy (DP) and secure aggregation, facilitating AI-driven insights without exposing sensitive user data. MLOps enhances the AI life cycle by leveraging distributed learning paradigms, including horizontal and Vertical Federated Learning (VFL), supported by Split Learning (SL). AIaaS extends these capabilities by exposing AI models and services through standardized APIs, enabling on-demand training, inference, and automation. By integrating AIaaS with DataOps and MLOps, networks can achieve greater intelligence, adaptability, and compliance with privacy and interoperability standards. This paper introduces a coherent architectural framework and operational strategy for embedding AI-driven intelligence into 6G networks, with a focus on key innovations in data governance, AI model coordination, and service exposure.