rhoai-model-registry

name	rhoai-model-registry
metadata	{"author":"rhoai3-demo","version":"1.0.0","platform-family":"rhoai","platform-baseline":"repo","ocp-baseline":"repo","skill-group":"RHOAI Platform"}
description	Use when documenting, reviewing, or provisioning Red Hat OpenShift AI model registries: enabling the modelregistry DataScienceCluster component, setting registriesNamespace, creating and editing model registries, choosing default non-production or external PostgreSQL/MySQL databases, configuring database CA certificates, managing registry permissions for users, groups, and project service accounts, understanding generated registry RBAC, and deleting registries without removing connected databases. Do NOT use for model catalog source governance, user model registration/version/deployment workflows, model serving runtime configuration, or live cluster changes without the OpenShift safety guard.

RHOAI Model Registry

Use this skill to provision and secure access to OpenShift AI model registries for the active product baseline in docs/PLATFORM_BASELINE.md.

Source Grounding

Read references/source-capture.md before using product configuration details. Official Red Hat documentation is product authority. This skill adapts the official Managing model registries guide to this repo's GitOps and governance review model.

Scope

This skill covers:

• the difference between the model catalog and model registry
• enabling the modelregistry component on the DataScienceCluster
• setting registriesNamespace: rhoai-model-registries
• verifying the registry namespace and operator controller deployment
• creating model registries from the dashboard
• model registry resource naming constraints
• default non-production database versus external PostgreSQL or MySQL database selection
• external database host, port, username, password, database name, and CA certificate options
• editing model registry name, description, and database details
• granting registry access to OpenShift users, groups, and project service accounts
• generated registry RBAC resources and <model-registry-name>-users groups
• deleting model registries and preserving connected databases

Use other skills for adjacent work:

• rhoai-model-catalog-sources for AI hub catalog source governance
• rhoai-model-catalog-workflows for catalog discovery, performance evaluation, and starting catalog registration or deployment flows
• rhoai-model-registry-workflows for registering models, versions, ModelCar images, transfer jobs, metadata edits, deployments, archive, and restore
• rhoai-model-serving-platform for serving models from registries
• rhoai-dashboard-customization for dashboard visibility flags such as disableModelRegistry
• rhoai-users-groups-access for broader OpenShift AI user and group access
• rhoai-certificate-management for shared CA bundle behavior
• rhoai-api-tiers for modelregistry.opendatahub.io/v1beta1 support posture

Demo Policy

For this repo:

• Treat model registry as the governed metadata and lifecycle store between experimentation and serving.
• Use the default database only for demo, evaluation, development, or testing flows, and label it as non-production.
• For production-oriented European enterprise narratives, describe external PostgreSQL or MySQL as the production path.
• Do not store database passwords or uploaded certificate material directly in committed manifests.
• Prefer group-based access for repeatable demo roles; use individual users only for narrow exceptions.
• Do not grant system:authenticated access unless the README or operations note explicitly explains why all cluster users should access the registry.
• Treat registry deletion as a destructive action requiring explicit approval; connected databases are not removed by deleting the registry.
• Do not author a direct ModelRegistry custom resource manifest unless the exact API, fields, and support posture are verified against active Red Hat docs or live schema.

Workflow

1. Confirm the active baseline in docs/PLATFORM_BASELINE.md.
2. Read references/source-capture.md and references/official-doc-extraction.md.
3. Confirm the modelregistry component should be managed by OpenShift AI.
4. Enable or review the DataScienceCluster component configuration.
5. Decide whether the registry uses the default non-production database or an external PostgreSQL/MySQL database.
6. For external databases, document hostname format, port, credentials source, database name, and CA certificate handling.
7. Create or edit the registry through the documented dashboard workflow unless a schema-verified GitOps path exists.
8. Grant access through users, groups, or projects and review generated RBAC.
9. Verify the registry in Model registry settings or AI registry settings.
10. Validate with references/validation-checklist.md.

References

• references/source-capture.md
• references/official-doc-extraction.md
• references/validation-checklist.md
• examples/model-registry-patterns.md