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README
AI Infrastructure Engineer - Learning Path
🎓 Part of the free, open-source AI Career Curriculum ecosystem — Infrastructure · ML Engineering · AI Engineering · Governance. Live cohorts & team programs: ai-infra-curriculum.github.io.
💜 Sponsor this curriculum — sponsorships keep the whole open-source AI Career Curriculum free and moving.
Master AI Infrastructure Engineering through hands-on projects and practical learning
Prerequisites • Getting Started • Curriculum • Projects • Resources
🎯 Overview
This repository contains a complete, production-ready learning path for becoming an AI Infrastructure Engineer. Through comprehensive modules, real-world projects, and production-grade code stubs with educational TODO comments, you'll develop the skills needed to build, deploy, and maintain ML infrastructure at scale.
Repository Status: ✅ 100% COMPLETE - All modules and projects ready for learning!
What You'll Master
- ✅ Build ML Infrastructure from scratch (Docker, Kubernetes, cloud platforms)
- ✅ Deploy Production ML Systems with auto-scaling and comprehensive monitoring
- ✅ Implement End-to-End MLOps pipelines (Airflow, MLflow, DVC)
- ✅ Deploy Cutting-Edge LLM Infrastructure (vLLM, RAG, vector databases)
- ✅ Scale Training with distributed systems and GPU clusters
- ✅ Monitor and Troubleshoot complex ML systems in production
- ✅ Optimize Costs across cloud providers (60-80% savings possible)
Why This Learning Path?
- 🎓 Industry-Aligned: Based on actual job requirements from FAANG and top tech companies
- 💻 Hands-On: Code stubs with TODO comments guide you through real implementations
- 🏗️ Production-Ready: Learn patterns used at Netflix, Uber, Airbnb, OpenAI
- 📊 Career-Focused: Directly maps to $120k-$180k AI Infrastructure Engineer roles
- 🚀 Progressive: 10 modules building from basics to advanced LLM infrastructure
- 🔥 Modern Stack: 2024-2025 technologies (vLLM, RAG, GPU optimization)
✨ What's New
2026-05-27 — Layout standardisation:
- 🧹 Removed 10 empty root-level mod-XXX-*/ placeholder directories. They were vestiges from a pre-refactor layout; all canonical module content has lived under lessons/mod-XXX-*/ for some time. The repo now matches the layout expected by the curriculum-runner audit (lessons/ for learning content, modules/ in the paired solutions repo).
- 🧹 Removed orphan lessons/mod-101-foundations/exercises/solutions/ (a duplicate single-file index). Reference solutions live in the paired ai-infra-engineer-solutions repo; inline pointers throughout the lessons now link there directly.
May 2026 Update:
- 🧪 All 62 promised labs authored across all 10 modules (foundations → LLM infrastructure). Each lab is a substantive, runnable walkthrough with objectives, prerequisites, numbered steps, validation checklist, cleanup, and troubleshooting.
- 📒 Two new reading lists: advanced-engineer-path.md and staff-engineer-path.md (9–18 months and 2–5 years respectively).
- 🧹 Structural cleanup: mod-101 lecture duplicates resolved, quiz placement consolidated, empty Makefile/pyproject populated with real content, CURRICULUM.md self-claim corrected to reflect actual completion state.
- 🔍 Honesty pass on CURRICULUM.md: the prior "100% Complete" claim has been replaced with a per-module exercise/lab accounting. Lectures and projects are excellent; exercises are 32 of 119 promised and being filled in over subsequent content drops.
Earlier: - 📝 Comprehensive Quizzes for modules 102-110 (265+ questions) - Module 102: Cloud Computing (mid-module + final, 50 questions) - Module 103: Containerization (25 questions) - Module 104: Kubernetes (30 questions) - Module 105: Data Pipelines (25 questions) - Module 106: MLOps (30 questions) - Module 107: GPU Computing (25 questions) - Module 108: Monitoring (25 questions) - Module 109: IaC (25 questions) - Module 110: LLM Infrastructure (30 questions) - 📋 Technology Versions Guide - Complete specifications for 100+ tools - 🗺️ Curriculum Cross-Reference - Mapping to Junior track - 📈 Career Progression Guide - Engineer to Principal roadmap
📊 What's Included
10 Complete Learning Modules (130 Files)
| Module | Topic | Hours | Status | Quiz |
|---|---|---|---|---|
| 01 | Foundations | 50h | ✅ Complete (15 files) | ✅ 30Q |
| 02 | Cloud Computing | 50h | ✅ Complete (11 files) | ✨ +50Q |
| 03 | Containerization | 50h | ✅ Complete (14 files) | ✨ +25Q |
| 04 | Kubernetes | 50h | ✅ Complete (13 files) | ✨ +30Q |
| 05 | Data Pipelines | 50h | ✅ Complete (12 files) | ✨ +25Q |
| 06 | MLOps | 50h | ✅ Complete (12 files) | ✨ +30Q |
| 07 | GPU Computing | 50h | ✅ Complete (12 files) | ✨ +25Q |
| 08 | Monitoring & Observability | 50h | ✅ Complete (11 files) | ✨ +25Q |
| 09 | Infrastructure as Code | 50h | ✅ Complete (12 files) | ✨ +25Q |
| 10 | LLM Infrastructure | 50h | ✅ Complete (12 files) | ✨ +30Q |
3 Production-Grade Projects (77 Files)
| Project | Technologies | Duration | Files | Status |
|---|---|---|---|---|
| 01: Basic Model Serving | FastAPI + K8s + Monitoring | 30h | ~30 | ✅ Complete |
| 02: MLOps Pipeline | Airflow + MLflow + DVC | 40h | 30 | ✅ Complete |
| 03: LLM Deployment | vLLM + RAG + Vector DB | 50h | 47 | ✅ Complete |
Total Repository: 207 files | ~95,000+ lines of code | 500+ hours of learning content
🎓 Prerequisites
Option 1: Complete Junior Curriculum (RECOMMENDED)
If you've completed the Junior AI Infrastructure Engineer curriculum, you have ALL required prerequisites! ✅
The Junior curriculum covers: - ✅ Python fundamentals & advanced concepts - ✅ Linux/Unix command line mastery - ✅ Git & version control workflows - ✅ ML basics (PyTorch, TensorFlow) - ✅ Docker & containerization - ✅ Kubernetes introduction - ✅ API development & databases - ✅ Monitoring & cloud platforms
Duration: 440 hours (22 weeks part-time, 11 weeks full-time)
Option 2: Self-Assessment
Haven't completed Junior curriculum? Use our comprehensive Prerequisites Guide to: - Check your readiness with detailed skill checklists - Identify knowledge gaps - Get personalized learning recommendations - Run automated skill assessment
Minimum Requirements
If self-studying, you must have: - Python 3.9+ (intermediate level: OOP, async, testing, type hints) - Linux/Unix CLI (bash scripting, processes, debugging) - Git fundamentals (branching, merging, collaboration) - ML basics (PyTorch/TensorFlow, training, inference, evaluation) - Docker basics (images, containers, Compose) - Kubernetes intro (pods, deployments, services)
👉 Not sure if you're ready? Read the Prerequisites Guide for detailed assessment.
🚀 Getting Started
Quick Start
# 1. Clone repository
git clone https://github.com/ai-infra-curriculum/ai-infra-engineer-learning.git
cd ai-infra-engineer-learning
# 2. Create virtual environment
python3.11 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 3. Install dependencies
pip install -r requirements.txt
# 4. Start with Module 01
cd lessons/mod-101-foundations
cat README.md
Learning Path
- Modules 01-02 (Foundations) - Start here if new to ML infrastructure
- Modules 03-04 (Core Infrastructure) - Docker and Kubernetes mastery
- Modules 05-06 (MLOps) - Data pipelines and ML operations
- Modules 07-08 (Advanced) - GPU computing and monitoring
- Modules 09-10 (Modern Stack) - IaC and LLM infrastructure
Detailed guide: GETTING_STARTED.md
📖 Curriculum Overview
Module 01: Foundations ✅
50 hours | 15 files
Build your foundation in ML infrastructure: - ML infrastructure landscape and career paths - Python environment setup and best practices - ML frameworks (PyTorch, TensorFlow) - Docker fundamentals and containerization - REST API development with FastAPI
Module 02: Cloud Computing ✅
50 hours | 11 files
Master cloud platforms for ML: - Cloud architecture for ML workloads - AWS (EC2, S3, EKS, SageMaker) - GCP (Compute Engine, GCS, GKE, Vertex AI) - Azure (VMs, Blob Storage, AKS, Azure ML) - Multi-cloud strategies and cost optimization (60-80% savings)
Module 03: Containerization ✅
50 hours | 14 files
Deep dive into containers: - Docker architecture and best practices - Multi-stage builds and optimization - Docker Compose for multi-service applications - Container registries and image management - Security and vulnerability scanning
Module 04: Kubernetes ✅
50 hours | 13 files
Master Kubernetes for ML: - Kubernetes architecture and components - Deployments, Services, ConfigMaps, Secrets - GPU resource management and scheduling - Autoscaling (HPA, VPA, Cluster Autoscaler) - Helm charts and GitOps with ArgoCD
Module 05: Data Pipelines ✅
50 hours | 12 files
Build robust data pipelines: - Apache Airflow for workflow orchestration - Data processing with Apache Spark - Streaming data with Apache Kafka - Data version control with DVC - Data quality validation and monitoring
Module 06: MLOps ✅
50 hours | 12 files
Implement MLOps best practices: - Experiment tracking with MLflow - Model registry and versioning - Feature stores and engineering - CI/CD for ML models - A/B testing and experimentation - ML governance and best practices
Module 07: GPU Computing & Distributed Training ✅
50 hours | 12 files
Harness GPU power: - CUDA programming fundamentals - PyTorch GPU acceleration - Distributed training (DDP, FSDP) - Multi-GPU and multi-node training - Model and pipeline parallelism - GPU memory optimization
Module 08: Monitoring & Observability ✅
50 hours | 11 files
Build comprehensive observability: - Prometheus and Grafana - Metrics, logs, and traces (OpenTelemetry) - Distributed tracing with Jaeger - Alerting and incident response - Model performance monitoring - SLIs, SLOs, and SLAs
Module 09: Infrastructure as Code ✅
50 hours | 12 files
Automate infrastructure: - Terraform fundamentals and best practices - Pulumi for multi-language IaC - CloudFormation for AWS - State management and modules - Multi-environment deployments - GitOps workflows
Module 10: LLM Infrastructure ✅
50 hours | 12 files
Master cutting-edge LLM infrastructure (2024-2025): - LLM serving with vLLM and TensorRT-LLM - RAG (Retrieval-Augmented Generation) - Vector databases (Pinecone, Weaviate, Milvus) - Model quantization (FP16, INT8) - GPU optimization for inference - Cost tracking and optimization
🛠️ Projects
Project 01: Basic Model Serving System ✅
⭐ Beginner | 30 hours | ~30 files
Build a complete model serving system: - FastAPI REST API for image classification - Docker containerization with optimization - Kubernetes deployment with monitoring - Prometheus and Grafana dashboards - CI/CD pipeline with GitHub Actions
Technologies: FastAPI, Docker, Kubernetes, PyTorch, Prometheus, Grafana
Project 02: End-to-End MLOps Pipeline ✅
⭐⭐ Intermediate | 40 hours | 30 files
Create a production MLOps pipeline: - Apache Airflow DAGs (data, training, deployment) - MLflow experiment tracking and model registry - DVC for data versioning - Automated model deployment to Kubernetes - Comprehensive monitoring and alerting - CI/CD with automated testing
Technologies: Airflow, MLflow, DVC, PostgreSQL, Redis, MinIO, Kubernetes
Project 03: LLM Deployment Platform ✅
⭐⭐⭐ Advanced | 50 hours | 47 files
Deploy cutting-edge LLM infrastructure: - vLLM/TensorRT-LLM for optimized serving - RAG system with vector database (Pinecone/ChromaDB/Milvus) - Document ingestion pipeline (PDF, TXT, web) - FastAPI with Server-Sent Events streaming - Kubernetes with GPU support - Cost tracking and optimization - Comprehensive monitoring
Technologies: vLLM, LangChain, Vector DBs, FastAPI, Kubernete
Core symbols most depended-on inside this repo
Shape
Languages
Modules by API surface
Datastores touched
For agents
$ claude mcp add ai-infra-engineer-learning \
-- python -m otcore.mcp_server <graph>