AI-DAPT

AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across
the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the
execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and
adapt based on their context. AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions. We have the following KPIs:

- 3 contributions in standardisation bodies for extensions/refinement of standards, based on the project’s research in Synthetic Data Generation, Data & Model Observability, AI Optimisation, Data & AI Pipeline execution, Hybrid Science-AI, Pipeline Interoperability, etc

Reason for applying to HSbooster.eu services

Given that AI-DAPT aims to enhance the European AI, Data, and Robotics ecosystem by sharing results and best practices to develop interoperable data artifacts, IT solutions, and tools, the project should approach both European and international standardisation bodies like CEN, DIN, OASIS, IEEE, W3C, and ISO, ensuring alignment with standards such as IEEE 1855, ISO/IEC 30170, and the ITU-T Y.3600 series. Contributions should be organised according to relevant standards and shared with standardisation bodies while the consortium partners should also engage in working groups like ISO/IEC JTC 1/SC 42 and IEEE P7000 to further promote standardisation.

The project needs support to achieve the aforementioned results. To this end, support is also needed to choose the appropriate standards, engage the correct stakeholders and try to influence processes and outcomes of standardisation, while interconnecting standards and regulations.