AI & Machine Learning

Learn Eval Ownership by Building a Tiny Lab Witness

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A case study explores building a name-normalization lab using a local Python script and a remote AI model without compromising sensitive data. The goal was to send only constrained requests to the remote service and maintain a local 'witness' file with hashes and results. This approach allows for cost-effective testing of AI models while preserving data privacy and control over the evaluation process.

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