Registers, versions, and manages machine learning models within the Red Hat OpenShift AI Model Registry.
This skill provides a comprehensive interface for managing the lifecycle of machine learning models within Red Hat OpenShift AI (RHOAI). It allows users to browse the Model Catalog, register new models, track metadata, and manage versions with associated storage URIs like S3 or Hugging Face. Designed for MLOps efficiency, it streamlines the promotion of models across environments—such as moving a model from development to production—and integrates directly with OpenShift MCP tools to ensure consistent artifact tracking and deployment readiness.
Key Features
01Support for S3, PVC, and Hugging Face storage URIs
02Model Catalog browsing and metadata tracking
03Automated fallback to OpenShift CRDs when RHOAI tools are unreachable
04Cross-environment promotion (Dev to Staging to Production)
055 GitHub stars
06Comprehensive model versioning and artifact management
Use Cases
01Registering a newly trained model and tracking its S3 storage location
02Listing model versions and comparing benchmark data before deployment
03Promoting a validated model version from a development namespace to production
