6G-DALI: an End-to-End AI Framework for Automating Data and ML Operations
Authors/Creators
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1.
Nextworks (Italy)
- 2. Nextworks
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3.
Intracom Telecom (Greece)
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4.
KU Leuven
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5.
Delft University of Technology
Description
Native support of Artificial Intelligence (AI) and Machine Learning (ML) is one of the pillars of future 6G networks. Despite the opportunities, there are several gaps that are hindering the seamless adoption of AI/ML in 6G. The lack of rich and high-quality datasets needed to train and fine-tune the models, and the need to test and evaluate AI models in a representative staging environment while ensuring ethics and regulatory compliance, represents a challenge without access to an end-to-end 6G testbed or a representative Digital Twin (DT) replica environment. To this end, 6G-DALI, the new SNS-JU Phase-3 project on Reliable AI for 6G Communications Systems and Services, aims at providing an end-to-end AI framework for 6G, structured around two interdependent pillars: AI experimentation as a service via Machine Learning Operations (MLOps) and Data and analytics collection and storage via Data Operations (DataOps). Finally, 6G-DALI will deliver a 6G Dataspace for dataset storage and secure sharing, and a Digital Twin (DT) testbed for data generation on demand.
Files
bernini-eucnc2025-6gdali.pdf
Files
(108.0 kB)
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Additional details
Dates
- Accepted
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2025-06