Cloudsmith released an ML Model Registry to manage models, datasets and artefacts with enterprise-grade controls to reduce model sprawl, compliance uncertainty and security risks. The registry applies the same rigour and policies used for software packages and containers to ML assets, enabling safer, more reliable workflows. It integrates with the Hugging Face Hub and SDK, permitting push, pull and management with familiar tooling while centralising control, compliance and visibility. Public models and datasets can be proxied and cached into Cloudsmith, exposing security and compliance data to Enterprise Policy Management for policy enforcement before production use. The launch responds to back‑doored models on public platforms and aims to add automated safeguards and traceability.
Northern Ireland's burgeoning tech scene is home to Cloudsmith, an organisation known for its cloud-native artefact (i.e. everything from developer documentation and annotations to design diagrams and schematics, compiled executables, libraries, logs and configuration files... and source code itself) management platform. The company has now worked to address ML, model sprawl, compliance uncertainty and security risks with the release of its Cloudsmith ML Model Registry.
The Cloudsmith ML Model Registry integrates directly with the Hugging Face Hub and SDK, enabling teams to push, pull, and manage models and datasets with familiar tooling while gaining centralised control, compliance and visibility. Public models and datasets can be proxied and cached from Hugging Face into Cloudsmith, where security and compliance data is made available to Enterprise Policy Management, enabling organisations to apply consistent policies before artefacts are used in development or production.
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