JFrog's newly available ML Model Management capabilities aim to bridge the gap between AI/ML model development and DevSecOps by introducing a novel approach to versioning models that benefits both Data Scientists and DevOps Engineers. These capabilities simplify the complex process of model versioning, addressing challenges associated with traditional Git-based approaches and offering a more intuitive system using name and timestamp-based versioning. By supporting Hugging Face APIs and enabling the classification of model versions as either "experimental" or "release," JFrog allows teams to track and manage model iterations efficiently, ensuring security and traceability. The platform also incorporates deduplication mechanisms to manage storage efficiently, akin to Docker images, and offers cleanup policies to remove outdated versions. With plans to extend support to other ML development solutions and enhance MLOps functionality, JFrog is committed to facilitating the deployment of high-quality, secure models through a trusted software supply chain.