CORDS: Collaborative Data Space for Manufacturing
Jan 2023 - Jan 2026
SEAI-funded research project developing a sovereign, interoperable data space and federated AI infrastructure for Ireland’s manufacturing sector, enabling organisations to collaborate on machine-learning models without sharing commercially sensitive data.
Problem Statement
- Manufacturers hold large volumes of energy data that could reveal efficiency gains, but it stays siloed because it is commercially sensitive.
- Manufacturing partners need secure model collaboration without surrendering data control.
- ML workflows require semantic interoperability to scale across organizations.
- Federated orchestration needs trustworthy execution and governance alignment.
What I Led
- Led design and implementation of a minimal viable data space infrastructure and semantic library for ML workflows.
- Designed and implemented decentralized federated ML services over data space protocol components.
- Defined technical pathways for governance-aware model lifecycle operations.
Deliverables
- Data space software stack and semantic assets
- Federated ML orchestration services
- Manufacturing-oriented model exchange workflows
Tech Stack
PythonJavaEESPARQLApache CamelOPCUADockerKubernetesPyTorchFLOWER.AIMLFlowKafkaIDSA DSP
Industry Relevance
Demonstrates how manufacturing ecosystems can operationalize AI collaboration while preserving sovereignty, governance, and interoperability.
Funding
- Programme
- SEAI Research, Development & Demonstration Funding Programme 2021
- Funder
- Sustainable Energy Authority of Ireland (SEAI)
- Grant number
- 21/RDD/688
- Recipient
- Nimbus Research Centre, Munster Technological University