rhoai-mlflow

name	rhoai-mlflow
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, installing, configuring, or operating MLflow in Red Hat OpenShift AI from the official Working with MLflow guide: shared cluster MLflow instance, project-to-workspace mapping, Kubernetes RBAC authorization, DataScienceCluster mlflowoperator enablement, MLflow and MLflowConfig custom resources, SQLite/PVC development deployments, PostgreSQL and S3-compatible production-oriented deployments, aggregate ClusterRoles, MLflow pseudo-resources, SDK installation and authentication, kubernetes-namespaced auth, local workstation and pod environment variables, version compatibility, experiment tracking, and project-specific artifact storage. Do NOT use for project/workbench lifecycle (use rhoai-project-workflows), generic S3 object operations (use rhoai-s3-object-storage-data), OpenShift AI model registry workflows (use rhoai-model-registry or rhoai-model-registry-workflows), formal custom evaluation workflows (use rhoai-model-evaluation), Gen AI playground prompt workflows outside MLflow availability checks (use rhoai-gen-ai-playground), or live cluster changes without the OpenShift safety guard.

RHOAI MLflow

Use this skill for Red Hat OpenShift AI MLflow platform and SDK workflows on the active product baseline in docs/PLATFORM_BASELINE.md.

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 MLflow guide to this repo's demo workflow and GitOps review model.

Scope

This skill covers:

• MLflow concept model in OpenShift AI
• single shared MLflow instance deployed through the MLflow Operator