Data Provenance for Teams That Actually Ship Models
Machine learning work breaks down in unglamorous places. A dataset quietly changes, a training run can’t be reproduced, or a review asks where a sample came from and nobody can answer with confidence. Walrus and Sui together point toward a workflow where heavy data stays off-chain, but commitments and access rules remain verifiable. That matters because “trust me” is not a provenance strategy once multiple teams and vendors touch the same corpus.
The practical win is the ability to reference a specific snapshot and treat that reference as a real object, not a filename in a shared drive. Permissions can be explicit, time-bound, and auditable, which helps when sensitive data is involved. Costs become easier to reason about too, because retention and availability can be defined up front instead of handled through ad-hoc duplication every time someone wants to feel safe. Even model documentation gets sharper when you can point to a stable dataset identity that doesn’t drift as buckets and folders get reorganized. Governance works better when it’s built into the workflow, not taped on after a crisis.
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