rhoai-model-registry-workflows

name	rhoai-model-registry-workflows
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 operating data scientist and AI engineer workflows in the Red Hat OpenShift AI model registry: registering models, registering model versions, setting source model format metadata, recording object storage or URI locations, registering and storing models as OCI ModelCar images, monitoring/retrying/deleting model transfer jobs, viewing model and version metadata, editing labels/descriptions/custom properties, deploying model versions from the registry, editing or deleting deployments, and archiving/restoring models or versions. Do NOT use for model registry administrator provisioning, database configuration, catalog source governance, deployment wizard details after registry handoff (use rhoai-model-deployment), or live cluster changes without the OpenShift safety guard.

RHOAI Model Registry Workflows

Use this skill for OpenShift AI model registry workflows performed by data scientists and AI engineers after an administrator has provisioned registry access.

Source Grounding

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

Scope

This skill covers:

• registering a new model and its first version from the dashboard
• registering additional model versions
• recording object storage or URI model locations
• registering and storing a model as an OCI ModelCar image
• monitoring, retrying, and deleting model transfer jobs
• viewing registered models and versions
• editing model-level and version-level metadata
• deploying model versions from a registry
• editing deployment properties for deployments created from the registry
• deleting deployments created from registry versions
• archiving and restoring models and model versions

Use other skills for adjacent work:

• rhoai-model-registry for administrator provisioning, database choices, permissions, generated RBAC, and registry deletion
• rhoai-model-catalog-sources for governing which catalog models are visible before registration
• rhoai-model-catalog-workflows for model catalog discovery, performance evaluation, tensor variant assessment, and starting catalog registration or deployment flows
• rhoai-model-serving-platform for serving runtime prerequisites and the serving platform configuration beyond the registry handoff
• rhoai-model-deployment for deploying registered model versions, deployment wizard behavior, routes, token authentication, deployment strategy, and inference endpoint smoke tests
• rhoai-automl for AutoML leaderboard registration, saved prediction notebooks, and AutoGluon serving runtime handoff
• rhoai-model-customization-training for registering and deploying customized model versions after fine-tuning workflows
• rhoai-model-management-monitoring for operating deployed models and metrics
• rhoai-connection-types for connection type templates used by object storage or model locations
• rhoai-mlflow for MLflow registered model APIs, experiments, prompts, datasets, traces, and SDK authentication when using MLflow rather than the OpenShift AI model registry
• rhoai-api-tiers for model registry API support posture

Demo Policy

For this repo:

• Treat the registry as the governed model lifecycle record for demo assets: source, version, metadata, deployment handoff, archive, and restore.
• Prefer metadata that helps enterprise review: owner, description, labels, license, source, benchmark or evaluation references, and deployment intent.
• Do not register a model location that points to a bucket root.
• Label URI-registered deployment limits clearly: deployment of URI-registered models is currently supported for public OCI repositories only.
• Keep source and destination credentials out of committed files and logs.
• Treat model transfer jobs as ephemeral operational resources; deleting a transfer job does not remove successfully transferred model artifacts.
• Do not archive models or versions that still have deployments. Delete the deployments first.
• Use the model serving skill when changing runtime parameters, route exposure, token authentication, model server size, replicas, or serving-runtime arguments.
• Do not modify protected port or model serving runtime arguments during redeploys; the official workflow warns that overriding them can break deployments.

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 user has access to an available model registry.
4. Decide whether the task is registration, versioning, ModelCar storage, transfer-job operation, metadata edit, deployment handoff, or archive/restore.
5. Use the dashboard paths and constraints in examples/model-registry-workflow-patterns.md.
6. For deployment operations, hand off deployment workflow details to rhoai-model-deployment and platform/runtime configuration details to rhoai-model-serving-platform.
7. Validate with references/validation-checklist.md.

References

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