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Security protocols and access controls specifically designed for protecting machine learning models, datasets, and associated infrastructure.
Distinct from Machine Access Tokens: None of the candidates relate to the security of ML infrastructure; they focus on malware analysis or general access tokens.
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This repository serves as the documentation source for the Hugging Face Hub, a collaborative platform designed for hosting, versioning, and discovering machine learning models, datasets, and interactive applications. It provides the foundational infrastructure for managing machine learning assets through Git-based repositories, which support large file storage, branching, and comprehensive commit history. The platform distinguishes itself by integrating metadata-driven discovery and structured management systems that allow users to attach licensing, task categories, and performance metrics to
Protecting assets through access tokens, GPG commit signing, malware scanning, and gated access requirements for sensitive datasets and models.