# ai-infra-curriculum/ai-infra-engineer-learning

AI Infrastructure Engineer Learning Track - Production ML infrastructure curriculum (2-4 years experience)

Repository: https://github.com/ai-infra-curriculum/ai-infra-engineer-learning
Canonical: https://ross.abutalabs.com/products/ai-infra-engineer-learning
Language: Python
License: MIT
License Family: permissive
Topics: ai-infrastructure, kubernetes, machine-learning, mlops, curriculum, learning, ai, career-development, devops, education, engineer, learning-resources, sre
Last push: 2026-06-26T02:07:29+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 89, release rhythm 35, longevity 22
- inputs: {"age_days": 312, "days_push": 69, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1634, forks 277 (observed 2026-08-28T04:05:14.237843+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: developer-tools, documentation
- domain: machine-learning, education, cloud-computing, large-language-models
- platform: cross-platform, python
- tags: curriculum, mlops, ai-infrastructure, career-development, sre, gpu-clusters, llm-serving, hands-on-projects, devops, containers, kubernetes, docker

## Member repositories
- ai-infra-curriculum/ai-infra-engineer-learning (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:14.237843+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:47:03.256779+00:00, confidence not recorded.
  - readme: https://github.com/ai-infra-curriculum/ai-infra-engineer-learning (fetched 2026-08-28T04:05:14.237843+00:00, sha e539224df8bd)
- Data as of 2026-08-30T08:39:29.467469+00:00.