🎓 Live cohorts & team programs → ai-infra-curriculum.github.io
The curriculum in these repositories is free and open-source. For live, instructor-led cohorts and corporate team programs, visit the site: Join the first cohort · For teams.
The content in these repositories is generated with AI assistance and undergoes ongoing human review. It may contain errors or outdated information. Treat it as a learning resource: cross-reference official docs, test code in a safe environment, and report issues via GitHub Issues or Discussions.
A comprehensive, hands-on learning path for AI Infrastructure Engineers — from entry-level to executive roles, plus a dedicated Agentic AI specialization vertical.
This curriculum provides production-focused training for AI Infrastructure Engineers, covering everything from foundational Python and Kubernetes to distributed training, LLM infrastructure, MLOps, platform engineering, security, enterprise architecture, and agentic AI systems.
This org spans 11 role tracks — the AI infrastructure ladder from Entry to Principal. Every track has a paired learning repository (modules, lecture chapters, exercises, quizzes) and a solutions repository with reference walkthroughs. Agentic-engineering and governance roles live in the sibling orgs.
At a glance:
- 🏢 22 curriculum repositories (11 learning + 11 solutions) plus support repos
- 📚 11 learning tracks with 11 paired solutions repositories
- 🎓 Hundreds of hands-on exercises and dozens of real-world projects
- 🤖 Agentic AI track complete — all four rungs have full learning content and reference solutions
This org covers AI infrastructure — running the platforms (Kubernetes, GPUs, training infra, serving, MLOps, IaC, SRE). Three sibling orgs cover the rest of the AI landscape, organized by what you do relative to a model:
- ML Engineering Curriculum — building & training the models: data, fine-tuning, pretraining, RLHF, evals.
- AI Engineering Curriculum — building with AI: agentic AI developer → engineer → senior → systems architect.
- AI Governance Curriculum — governing, securing & assuring AI: security, chief AI officer, evaluation & agentic safety.
| Role | Focus | Repositories |
|---|---|---|
| Junior Engineer | Python & ML basics, Linux & Docker, Kubernetes intro, cloud platforms, monitoring | 📘 Learning · ✅ Solutions |
| Engineer | Production ML, distributed training, GPU computing, advanced Kubernetes, MLOps, LLM infra, IaC | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| MLOps Engineer | CI/CD for ML, model registry, feature stores, experiment tracking, drift detection, A/B testing | 📘 Learning · ✅ Solutions |
| ML Platform Engineer | Platform architecture, multi-tenancy, serving at scale, platform APIs/SDKs, developer experience | 📘 Learning · ✅ Solutions |
| Performance Engineer | GPU utilization, inference latency, training efficiency, cost optimization, profiling | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| Senior Engineer | Advanced Kubernetes (operators/CRDs), distributed training at scale, CUDA, multi-cloud, SRE | 📘 Learning · ✅ Solutions |
| Architect | Enterprise ML architecture, multi-cloud/hybrid, security & compliance, FinOps, HA/DR, LLM/RAG platforms | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| Team Lead | Technical strategy & roadmaps, team building, ADRs, incident & performance management | 📘 Learning · ✅ Solutions |
| Senior Architect | Cross-org architecture alignment, enterprise standards, multi-year roadmaps, executive communication | 📘 Learning · ✅ Solutions |
| Role | Focus | Repositories |
|---|---|---|
| Principal Engineer | Deep technical expertise, extreme-scale distributed systems, novel infra solutions, mentorship | 📘 Learning · ✅ Solutions |
| Principal Architect | Company-wide strategy, multi-year roadmaps, technology selection, architecture governance | 📘 Learning · ✅ Solutions |
All 11 infrastructure tracks are actively maintained. Work focuses on depth, runtime validation, and human review. The agentic-engineering and governance roles now live in their sibling orgs (see Curriculum Family above). See the Career Progression Guide for role and skill mapping.
-
Choose a track based on your experience level and career direction.
-
Clone the learning repository:
git clone https://github.com/ai-infra-curriculum/ai-infra-junior-engineer-learning.git cd ai-infra-junior-engineer-learning -
Start with Module 001 and read its
README.md. -
Work through the exercises in each module.
-
Check the companion solutions repository for reference walkthroughs.
- Languages: Python, Bash, HCL (Terraform), YAML
- ML frameworks: PyTorch, TensorFlow, scikit-learn
- Orchestration: Kubernetes, Helm, ArgoCD, FluxCD
- Cloud & containers: AWS, GCP, Azure, Docker, containerd
- MLOps: MLflow, Kubeflow, DVC, Feast
- Observability: Prometheus, Grafana, Loki, Jaeger
- IaC & CI/CD: Terraform, Pulumi, GitHub Actions, GitLab CI
- LLMs & GPU: vLLM, Llama, Mistral, RAG, CUDA, NCCL, TensorRT
Contributions are welcome across the organization:
- Fix broken links, stale references, or inaccurate explanations
- Add depth to modules, projects, or strategic artifacts
- Improve validation for runnable exercises and projects
- Report issues or ideas via GitHub Discussions
- Follow the
CONTRIBUTING.mdin the specific repository you want to improve
Most curriculum repositories are MIT-licensed. See the target repository's LICENSE file for authoritative terms.
- Issues: use the relevant repository's GitHub Issues
- Discussions: organization discussions
- Docs: Career Progression · Curriculum Cross-Reference
Maintained by VeriSwarm.ai
