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Expert3 to 5 days / 20+ guided hoursdraft
AI Automation with n8n, Ollama, LiteLLM, RAG, and ComfyUI: Expert
Design production AI automation with observability, security boundaries, cost controls, and capstone workflows.
Audience
- Self-paced technical learners
- Instructor-led cohorts
- Enterprise teams preparing staff for hands-on operations
Prerequisites
- Intermediate course or equivalent production experience
- Comfort with troubleshooting and design tradeoffs
Outcomes
- Provision an isolated ai automation with n8n, ollama, litellm, rag, and comfyui lab from template metadata.
- Use snapshots, rollback, validation checks, and teardown safely.
- Explain how n8n, Ollama, LiteLLM, Qdrant, ComfyUI fit into an enterprise training environment.
- Produce evidence that an instructor or admin can review.
Course plan
Modules and labs
Each module maps to provisioned lab work, validation evidence, reset/rollback policy, and instructor visibility.
Module 1
Production architecture
AI Automation with n8n, Ollama, LiteLLM, RAG, and ComfyUI Expert lab 1Module 2
Failure simulation and hardening
AI Automation with n8n, Ollama, LiteLLM, RAG, and ComfyUI Expert lab 2Module 3
Automation, DR, and capstone
AI Automation with n8n, Ollama, LiteLLM, RAG, and ComfyUI Expert capstoneRequired templates
n8n automation node
definedDocker host
Ollama/LiteLLM client node
definedUbuntu 24.04 LTS
Vector database/RAG node
definedUbuntu 24.04 LTS plus Qdrant or compatible vector DB
ComfyUI workflow node
definedGPU-capable Linux template where capacity permits
TODO: Publish only when GPU capacity and model licensing are approved for training tenants.
Validation checks
AI endpoint reachable
The model gateway returns at least one allowed model for the tenant.
Docker running
Docker daemon responds and can run a hello-world container.
VM reachable
The VM reports boot complete and responds through the tenant bastion path.