This project explores how Hyperledger Fabric can be used to enhance machine learning workflows by adding trust, transparency, and traceability across a distributed network.
- Enable auditable, tamper-proof ML workflows via blockchain.
- Track data origin, model versions, and training parameters.
- Share models across consortium members with immutable logs.
- Use smart contracts to enforce model lifecycle policies.
- Hyperledger Fabric: Private permissioned blockchain infrastructure.
- Smart Contracts (Chaincode): Encodes policies for model training and access.
- Model Metadata: Logged during training (data version, algorithm, config).
- Audit Logs: Immutable records of data usage and model updates.
- Trusted ML governance for enterprises
- Tamper-proof traceability for models and data
- Supports regulatory compliance and model reproducibility
- Collaborative model development across secure networks
- Financial fraud models shared between banks
- Supply chain optimizations across multiple vendors
- Healthcare ML models with logged data lineage
See the full strategy document:
📄 Blockchain Strategy.docx
MIT License – for educational and enterprise use cases.