ADR 0016: Adopt NVIDIA Certification Framework for Community AI Skills Development
Context
RegenNeighbourhood members need a clear AI skill path. The community lacks a structured skill-verification system.
D1: Adopt three Associate certs as baseline
The community adopts three NVIDIA Associate certifications:
- Generative AI LLM — LLM fundamentals, prompt engineering, RAG, fine-tuning.
- Agentic AI — multi-agent workflows, autonomous decision systems.
- AI Infrastructure and Operations — edge node management, GPU orchestration.
D2: Use free content for onboarding
Start with freely available NVIDIA learning paths:
- OpenUSD Foundations (11h free) — 3D digital twin literacy.
- Robotics Fundamentals (28h free) — farm automation foundation.
- NIM Microservices intro (2h free) — inference deployment.
- Decentralized AI with FLARE (4h free) — federated learning.
D3: Track progress via community skills registry
Members log course completions and badge IDs in a shared registry. Three states: Interest, In Progress, Certified.
Positive
- Clear vendor-aligned progression framework.
- Free content tracks lower the barrier for initial engagement.
- Associate certifications provide verifiable skill markers.
- Physical AI and digital twin tracks map to RegenNeighbourhood use cases.
Negative
- NVIDIA certifications need internet for exams.
- Exam costs (USD 125–400) may limit participation.
- Hardware-centric framework. May be overkill for pure software work.
- No community-specific content in standard NVIDIA curriculum.