AI Governance Lab

AI Governance Lab is an open-source research and engineering portfolio dedicated to building secure, compliant, and scalable Enterprise AI systems. We explore advanced MLOps, LLMOps, and Multi-Cloud Deployment Patterns with a strict focus on AI Governance, LLM-as-a-Judge evaluation, and regulatory compliance (GDPR, EU AI Act, SOC2, HIPAA).

From high-throughput vLLM inference on NVIDIA GPUs to Retrieval-Augmented Generation (RAG) and Quantum-Classical Hybrid Orchestration, this lab documents production-ready architecture patterns across AWS, Azure, Google Cloud (Vertex AI), and IBM watsonx.

Keywords: AI Governance, MLOps, LLMOps, vLLM, NVIDIA A100/H100, LLM-as-a-Judge, Quantum AI, Enterprise AI Agents, RAG, Multi-Cloud AI, Compliance, GDPR, SOC2

Scope

• Multi‑cloud AI agents: reusable deployment patterns (PowerVS, Kubernetes, hybrid).
• Retrieval Augmented Generation (RAG): infra + vector DB (Qdrant) + orchestration.
• Secure container build & delivery: scripts and base images under docker_images_for_ai_agents.
• Infrastructure as Code: Kubernetes clusters, Helm charts, IaC modules (k8s-*, ai-agent-s4hana-powervs-deployer).
• MLOps & Experiment Tracking: MLflow demos (k8s-mlflow-*).
• Model & Data Quality Monitoring: Evidently integration (k8s-evidently-demo).
• High-throughput & lightweight model serving (vLLM, k8s-ollama-fastapi-demo).
• Governance foundations: reproducibility, environment isolation, observability, policy readiness.

Repository Families

• ai-agent-s4hana-powervs-deployer: PowerVS + SAP integration deployment automation.
• docker_images_for_ai_agents: Curated base images for governed AI workloads.
• k8s-ai-agent-multicloud-rag-iac: RAG + agents infra as code.
• k8s-ai-agents-demo: Reference agent scenarios.
• k8s-evidently-demo: Monitoring & drift detection.
• k8s-mlflow-rancher-desktop / k8s-mlfow-demo: Local & cluster MLflow patterns.
• k8s-ollama-fastapi-demo: Lightweight model serving.

Principles

1. Governance by design: traceability, versioning, auditability.
2. Isolation & portability: multi-cloud ready artifacts.
3. Observability: metrics, drift, lineage.
4. Least privilege & supply chain hygiene.
5. Modularity: composable Helm charts / IaC units.

Getting Started

1. Pick a stack folder (e.g. k8s-ai-agent-multicloud-rag-iac).
2. Review README in that folder for prerequisites.
3. Build or pull the required base image from docker_images_for_ai_agents.
4. Deploy via Helm / IaC.
5. Extend with monitoring (Evidently) and tracking (MLflow).

Contributing

• Open issues for enhancements.
• Keep modules stateless & parameterized.
• Provide architecture diagrams when adding new vertical capabilities.

License

Each subfolder contains its license; default is permissive OSS (see included LICENSE files).

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Maintained by the AI Governance Lab organization.