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A lightweight, open-source ledger platform for recording machine-learning experiments across organizations and teams, enabling verifiable reproducibility, provenance, and auditability in federated and multi-party collaborations. MLTrail stores compac
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README.md

MLTrail Verifiable Provenance Ledger (MVP)

This repository contains a minimal, production‑oriented MVP for a verifiable provenance ledger tailored to federated machine learning experiments. It offers a compact, append-only hash-chain ledger with a contract-driven schema registry and a delta-sync protocol to keep distributed networks eventually consistent.

Key components (MVP):

  • Data contracts and schemas for Experiment, Run, Dataset, Model, Environment, EvaluationMetric, Policy.
  • Append-only ledger with hash-chain, genesis block, and delta-sync capability.
  • Lightweight contract registry (GoC bridge) concepts to enable canonical primitives and replay protection.
  • Adapters (MLFlow-like, WandB-like) that publish to the ledger.
  • Reproducibility groundwork via environment hashing and deterministic record layout.
  • Governance and security scaffolding via signatures (plausible placeholder for MVP).

How to use (quick start):

  • Run tests and build: ./test.sh
  • Publish a registry entry: use the ledger to add a ContractRegistryEntry payload.
  • Explore delta-sync: create two ledgers, publish on one, and retrieve deltas on the other with delta_sync.

Note: This is an MVP and intentionally lightweight. It focuses on correct data flow and verifiable append-only logs while keeping the surface area small for rapid iteration and extension (adapters, delta-sync, and governance).

For contributors: see mltrail_verifiable_provenance_ledger_for/goC_bridge.py for structure of the canonical bridge and mltrail_verifiable_provenance_ledger_for/ledger.py for the delta-sync primitives.

Version: 0.1.0

"README.md" hooks into packaging via pyproject.toml readme field.