Use Machine Learning (ML) Repositories in Artifactory to manage ML models with the same security, governance, and automation you apply to your traditional software packages. Using ML repositories and the FrogML Python library, you can connect AI/ML frameworks with Artifactory as a single source of truth to log, load, and manage models. This unification streamlines deployment, optimization, and governance, helping you scale trusted AI applications. For more information, see JFrog ML documentation.
Using ML repositories and FrogML in Artifactory provides the following benefits:
Secure Storage: Protect your proprietary information by deploying models and additional resources to Artifactory local repositories, giving you fine-grain control of the access to your models.
Format-Aware SDK: Use the FrogML SDK to log and load complete, ready-to-use ML models in several supported formats.
Rich Metadata and Search: Enhance models with rich metadata, including custom properties and automatically extracted attributes. Use the Artifactory Query Language (AQL) to easily search for and manage models based on their metadata.
Easy Collaboration: Manage and share ML models with teammates alongside all your other application dependencies in a single system.