ML500 MLOps Enablement Lab Deployment

This repository contains the necessary components for facilitators to deploy a complete MLOps Enablement lab environment on OpenShift and OpenShift AI (RHOAI).

Overview

The deployment sets up a comprehensive MLOps environment including:

• Red Hat OpenShift AI (RHOAI) for ML workspaces
• Source control using Gitea
• Model Registry for ML model management
• Feature Store (Feast) for feature management
• MinIO for object storage
• Monitoring and logging infrastructure
• User workspace configuration

Prerequisites

• OpenShift 4.x cluster with cluster-admin access
• Helm 3.x installed
• helm CLI tool configured with cluster access
• Sufficient cluster resources for running the workloads

Repository Structure

.
├── operators/          # OpenShift operator installations
├── student-content/    # Student workspace configurations
├── toolings/          # MLOps tools and monitoring setup
├── Containerfile      # Container build definition
└── install.sh         # Installation script

Components Installed

Operators

• OpenShift AI Operator
• GitOps Operator
• Service Mesh Operator
• Serverless Operator
• User Workload Monitoring
• Logging Operator
• Advanced Cluster Security

Tools and Services

• Data Science Projects
• Data Science Pipeline Architecture
• MinIO Object Storage
  • Preconfigured buckets: pipeline, models, data, data-cache
• Model Registry with MySQL backend
• Feast Feature Store
• Gitea Source Control
• Monitoring and Logging Stack

Deployment Instructions

Configuration

1. Update the cluster domain in student-content/values.yaml:

   cluster_domain: apps.your-cluster-domain.com

2. Set the number of attendees:

   attendees: <number_of_students>

Installation Methods

The installation script handles:

• Identity management setup
• Operator installations
• Component deployments
• User workspace configurations

./install.sh

Post-Installation

After successful deployment:

1. The environment will be available at https://console-openshift-console.apps.<cluster_domain>
2. Each student will have their own Data Science Project
3. Access credentials will be created for each configured attendee

Support

For issues or questions, please open a GitHub issue in this repository.

Contributing

If you'd like to contribute to this project, please submit a pull request with your proposed changes.