TY - GEN
T1 - Developing a 6G Data and ML Operations Automation via an End-To-End AI Framework
T2 - 14th Workshop on Mining Humanistic Data, MHDW 2025, 10th Workshop on B5G-Putting Intelligence to the Network Edge, BG5-PINE 2025, 2nd Workshop on AI Applications for Achieving the Green Deal Targets, AI4GD 2025, 1st Workshop on SilverTech: Empowering the Future of Ageing Through Advanced AI-Based Technologies, SilverTech 2025 and 5th Workshop on AI and Ethics, 2025, held as parallel events of the 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025
AU - Chochliouros, Ioannis P.
AU - Ramantas, Kostas
AU - Theodorou, Vasileios
AU - Pinto, Christian
AU - Kennouche, Takai Eddine
AU - Ksentini, Adlen
AU - Minucci, Franco
AU - Amaxiliatis, Dimitrios
AU - Hai, Rihan
AU - More Authors, null
PY - 2025
Y1 - 2025
N2 - One of the key enablers of 6G is the Native support of Artificial Intelligence (AI) and Machine Learning (ML) at all the system levels, components and mechanisms, from the orchestration and management levels to the low-level optimisation of the infrastructure resources including Cloud, Edge, RAN, Core Network, as well as a transport network. However, this integration presents significant challenges, primarily the need for relevant datasets to train AI models. The availability of high-quality 6G data is still limited, and even when new models are developed, testing and validation remain complex without adequate evaluation platforms. To address these challenges, the 6G-DALI project proposes a framework that harmonizes Data Management with AI development. Its approach is defined by two “key” pillars: (i) AI experimentation as a service via MLOps and; (ii) Data and analytics collection and storage via DataOps. The 6G-DALI DataOps pillar provides the mechanisms for preparing clean and processed data that are stored within a 6G Dataspace and are made available for training and validating machine learning models as a service, a part of the MLOps Pillar. The end-to-end framework also delivers continuous monitoring, drift detection and retraining of models. 6G-DALI revolutionises next-generation 6G networks by addressing the critical challenges of data availability, artificial intelligence integration and energy efficiency.
AB - One of the key enablers of 6G is the Native support of Artificial Intelligence (AI) and Machine Learning (ML) at all the system levels, components and mechanisms, from the orchestration and management levels to the low-level optimisation of the infrastructure resources including Cloud, Edge, RAN, Core Network, as well as a transport network. However, this integration presents significant challenges, primarily the need for relevant datasets to train AI models. The availability of high-quality 6G data is still limited, and even when new models are developed, testing and validation remain complex without adequate evaluation platforms. To address these challenges, the 6G-DALI project proposes a framework that harmonizes Data Management with AI development. Its approach is defined by two “key” pillars: (i) AI experimentation as a service via MLOps and; (ii) Data and analytics collection and storage via DataOps. The 6G-DALI DataOps pillar provides the mechanisms for preparing clean and processed data that are stored within a 6G Dataspace and are made available for training and validating machine learning models as a service, a part of the MLOps Pillar. The end-to-end framework also delivers continuous monitoring, drift detection and retraining of models. 6G-DALI revolutionises next-generation 6G networks by addressing the critical challenges of data availability, artificial intelligence integration and energy efficiency.
KW - 5G
KW - 6G
KW - AI as-a-service (AIaaS)
KW - AI native
KW - Artificial Intelligence (AI)
KW - DataOps
KW - Digital Twin (DT)
KW - Gaia-X
KW - Generative AI
KW - International Data Space (IDS)
KW - Large Language Models (LLM)
KW - Machine Learning (ML)
KW - ML Operations (MLOps)
UR - http://www.scopus.com/inward/record.url?scp=105010096713&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-97317-8_10
DO - 10.1007/978-3-031-97317-8_10
M3 - Conference contribution
AN - SCOPUS:105010096713
SN - 9783031973161
T3 - IFIP Advances in Information and Communication Technology
SP - 129
EP - 144
BT - Artificial Intelligence Applications and Innovations. AIAI 2025 IFIP WG 12.5 International Workshops - B5G-PINE 2025, Proceedings
A2 - Papaleonidas, Antonios
A2 - Pimenidis, Elias
A2 - Papadopoulos, Harris
A2 - Chochliouros, Ioannis
PB - Springer
CY - Cham
Y2 - 26 June 2025 through 29 June 2025
ER -
