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.