CORDS: Collaborative Data Space for Manufacturing
Jan 2023 - Jan 2026
Federated learning platform on International Data Space protocol for self-sovereign, cross-party model orchestration in manufacturing.
Problem Statement
- 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
Architecture Placeholder
Diagram slot for system architecture and data flow.
PythonJavaEESPARQLApache CamelOPCUADockerKubernetesPyTorchFlowerMLflowKafka
Industry Relevance
Demonstrates how manufacturing ecosystems can operationalize AI collaboration while preserving sovereignty, governance, and interoperability.
Problem framing
Cross-party AI collaboration fails when data sovereignty, interoperability, and trust are treated separately. CORDS integrated these concerns into one architectural track.
Delivery pattern
- Data space-first architecture
- Semantics-driven interoperability
- Federated services with enforceable policy controls
Why it matters
The pattern enables industrial partners to move from isolated pilots toward governed, production-oriented ML collaboration.