My AI Journey

This repository documents my journey into AI, MLOps, infrastructure automation, and GPU-accelerated compute.
It includes Terraform-provisioned lab environments (TXGrid), machine learning projects, and operational tooling like Ansible.

Repository Structure

.
├── infra/
│   ├── terraform/      # Infrastructure as Code (TXGrid provisioning)
│   └── ansible/        # Configuration management
├── labs/               # Jupyter notebooks, experiments
├── projects/           # End-to-end ML/AI projects
├── notes/              # Study notes & documentation
└── README.md

Goals

1. Build a full on-prem "AI lab" (TXGrid)

• Terraform VM provisioning\
• Cloud-init automation\
• libvirt networking\
• GPU workstation tuning\
• Ansible configuration management

2. Learn and practice AI/ML fundamentals

• Python + NumPy + Pandas\
• Classical ML\
• Deep learning

3. Move into MLOps, LLMs, and production concepts

• Model serving\
• Vector databases\
• RAG pipelines\
• Kubernetes + GPUs

4. Document everything thoroughly

Infrastructure

Terraform lives in:

infra/terraform/

Ansible lives in:

infra/ansible/

Roadmap

Short-term

• Add Ansible playbooks\
• Add Terraform README\
• Add GPU benchmarking notebook

Medium-term

• Deploy Kubernetes on TXGrid\
• GPU operator\
• LLM inference pipeline

Long-term

• Full AI platform on TXGrid\
• Local RAG engine\
• Blog writeups