Modern DevOps for AI Teams: GitOps, Infrastructure-as-Code, and GPU Cluster Management at Scale
Traditional DevOps manages stateless web services. AI DevOps manages stateful training jobs, GPU scheduling, multi-gigabyte model artifacts, and experiment tracking — a fundamentally different operational profile.
1. AI-Specific DevOps Challenges
| Challenge | Web DevOps | AI DevOps |
|---|---|---|
| Artifact Size | ~50MB Docker image | 10-100GB model weights |
| Resource Type | CPU + RAM | GPU (A100/H100) scheduling |
| Reproducibility | Dockerfile | Dockerfile + dataset version + random seed + hyperparams |
| Rollback | Revert container tag | Revert model version + feature store state |
2. GitOps for Model Deployment
# model-deployment.yaml (ArgoCD managed)
apiVersion: apps/v1
kind: Deployment
metadata:
name: inference-server
annotations:
model-version: "v2.3.1"
model-registry: "s3://models/production/llama-fine-tuned-v2.3.1"
spec:
replicas: 3
template:
spec:
containers:
- name: vllm-server
image: vllm/vllm-openai:latest
resources:
limits:
nvidia.com/gpu: 1
env:
- name: MODEL_PATH
value: "s3://models/production/llama-fine-tuned-v2.3.1"
Treating AI infrastructure as code ensures reproducibility, auditability, and rapid rollback when model performance degrades.
