ai-infra-curriculum/ai-infra-engineer-learning resource

Health v2 · maintenance only

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1634 stars · 277 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A free, open-source curriculum for becoming an AI Infrastructure Engineer, with 10 modules and 3 hands-on projects covering Docker, Kubernetes, MLOps pipelines, LLM serving, and GPU cluster training. It targets engineers with 2-4 years of experience and includes production-grade code stubs with educational TODO guidance.

Use cases

• learn ai infrastructure engineering from scratch
• study mlops pipelines with airflow mlflow and dvc
• practice deploying llm inference with vllm and rag
• learn kubernetes for machine learning workloads
• prepare for an ai infrastructure engineer job interview
• understand gpu cluster and distributed training
• reduce cloud costs for ml systems

When to choose

• you want a structured, project-based path into ML/AI infrastructure
• you have 2-4 years of engineering experience and want to specialize in MLOps or AI infra
• you prefer free, self-paced curriculum aligned with industry job requirements

When to avoid

• you need a production tool or library rather than learning material
• you are a complete beginner without prerequisites in Python, Docker, or cloud basics
• you need an accredited certification or instructor-led course

Facets

learning-resource · maturity active

developer-tools documentation machine-learning education cloud-computing large-language-models cross-platform python curriculum mlops ai-infrastructure career-development sre gpu-clusters llm-serving hands-on-projects devops containers kubernetes docker

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