Review and integrate OpenShift AIOps Self-Healing Platform pattern documentation (PR #652) #653

Overview

PR #652 introduces comprehensive documentation for the OpenShift AIOps Self-Healing Platform validated pattern - an AI-powered, GitOps-deployed solution providing intelligent, automated incident response for OpenShift clusters.

PR: #652
Author: @tosin2013
Size: XXL (3,542 additions, 0 deletions)
Status: Open - awaiting review

Documentation Scope

The PR adds the following documentation components:

Core Documentation

• Pattern overview and introduction
• Getting started guide with deployment instructions
• Architecture documentation covering:
  • Coordination Engine
  • MCP Server integration
  • KServe model serving

Deployment Guides

• High Availability (HA) cluster deployment
• Single Node OpenShift (SNO) deployment
• Cluster sizing recommendations (HA: 7 nodes, ~57 cores, ~185GB RAM)

Configuration & Customization

• Extensive customization guide (1,600+ lines) with real-world production examples:
  • Multi-model inference serving (anomaly detection, predictive analytics)
  • Mixed storage configurations (CephFS + gp3-csi)
  • Platform configuration tuning via ConfigMap
  • Complete self-healing workflow
• Storage configuration patterns (ODF, CephFS, cloud storage)

Development Resources

• Jupyter notebook catalog (33 notebooks)
• Development workflow documentation
• Workshop materials and references

Review Checklist

Documentation team should verify:

• Hugo build: Confirm site builds successfully without errors (currently builds 483 pages)
• AsciiDoc formatting: No parsing errors, proper use of modular doc structure
• Code examples: All YAML/Python code examples are properly formatted and functional
• Cross-references: All internal links and cross-references are valid
• Real-world examples: Verify examples align with current cluster deployments
• External links: Check GitHub pattern repo and workshop links are correct and stable
• Technical accuracy: Validate cluster sizing and storage configuration recommendations
• Style consistency: Ensure documentation follows existing pattern documentation style
• Accessibility: Review AsciiDoc formatting for accessibility compliance

Action Items

1. Initial Review: Documentation team to review structure and formatting
2. Technical Review: SMEs to verify technical accuracy of architecture, sizing, and configuration
3. QE Review: Quality Engineering to test examples and verify build process
4. Final Approval: Obtain approval from reviewers (mention: Andrew Beekhof, Drew Minnear if appropriate)
5. Squash commits: Ensure all commits are squashed before merge
6. Merge: Complete integration into main documentation

Success Criteria

• All documentation builds without errors
• Real-world examples validated against current deployment
• Reviewers provide approval
• No broken cross-references
• Consistent with existing documentation style and guidelines

Additional Information

• Contribution guidelines: https://validatedpatterns.io/contribute/contribute-to-docs/
• Contact: hybrid-cloud-patterns@googlegroups.com