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Document how to do SLSA for ML and highlight gaps #978
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Thanks for the initial thoughts on this. This seems worth documenting indeed. I agree strongly with the assertion that any transformation process --> build and that data inputs seem to map well to dependencies. |
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There have been some questions as to what "SLSA for ML" looks like. This issue attempts to give a short synopsis so that we can hopefully agree and turn that into durable documentation.
First, Machine Learning (ML) models fit the SLSA Model at a high level:
This is not obvious to most readers, so we should document it.
Second, ML highlights some gaps or challenges in SLSA that are not really specific to ML but may be a higher priority or more painful for ML. They include:
All of these are surmountable, but it's worth documenting.
Any thoughts in agreement or disagreement? I'll try to update this top post with the consensus. If you have other challenges, I can add them as well.
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