ai-infra-engineer-learning

AI Infrastructure Engineer - Learning Path

🎓 Part of the free, open-source AI Career Curriculum ecosystem — Infrastructure · ML Engineering · AI Engineering · Governance · Startup · Workforce.

💜 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

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🎯 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)

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✨ 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

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📊 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

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🎓 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.

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🚀 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

1. Modules 01-02 (Foundations) - Start here if new to ML infrastructure
2. Modules 03-04 (Core Infrastructure) - Docker and Kubernetes mastery
3. Modules 05-06 (MLOps) - Data pipelines and ML operations
4. Modules 07-08 (Advanced) - GPU computing and monitoring
5. Modules 09-10 (Modern Stack) - IaC and LLM infrastructure

Detailed guide: GETTING_STARTED.md

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📖 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

View Module 01 →

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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)

View Module 02 →

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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

View Module 03 →

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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

View Module 04 →

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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

View Module 05 →

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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

View Module 06 →

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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

View Module 07 →

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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

View Module 08 →

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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

View Module 09 →

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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

View Module 10 →

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🛠️ 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

View Project 01 →

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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

View Project 02 →

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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, Kubernetes + GPU, Transformers

View Project 03 →

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💰 Cost Considerations

Cloud Costs

All learning materials can be completed within free tier limits:

• AWS: 750 hours/month t2.micro + $300 credits (varies)
• GCP: $300 credit (90 days)
• Azure: $200 credit (30 days)

GPU costs (optional, for advanced projects):

• On-demand: $1-3/hour
• Spot instances: $0.30-1/hour (70% savings)
• Estimated total: $50-150 for complete curriculum

Optimization Tips

• Use spot instances for training (60-90% savings)
• Leverage free tiers across multiple cloud providers
• Delete resources when not in use
• Use local development where possible

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📚 Resources

Included Documentation

• Comprehensive lesson materials with examples
• Code stubs with TODO comments for guided implementation
• Complete project specifications with architecture diagrams
• Quizzes and assessments for each module
• Best practices and design patterns

External Resources

• 📖 Reading Lists: resources/reading-lists/ — advanced + staff-engineer paths
• 🛠️ Cheat Sheets: resources/cheat-sheets/ — docker, kubernetes, git, linux, python infrastructure
• ❓ FAQ: resources/faq.md

Curriculum Documentation

• 📋 Technology Versions Guide - Recommended versions for all tools and frameworks
• 🗺️ Curriculum Cross-Reference - Mapping between Junior and Engineer tracks
• 📈 Career Progression Guide - Complete career ladder from Junior to Principal

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🎯 Learning Outcomes & Career Impact

After Completion, You'll Be Qualified For:

AI Infrastructure Engineer

• 💰 Salary: $120,000 - $180,000
• 🏢 Companies: Tech companies, AI startups, ML-focused organizations
• 📈 Demand: Very high (growing 35% year-over-year)

ML Platform Engineer

• 💰 Salary: $130,000 - $190,000
• 🏢 Companies: Large tech firms, enterprises with ML teams
• 📈 Demand: High (specialized role)

MLOps Engineer

• 💰 Salary: $110,000 - $170,000
• 🏢 Companies: All organizations doing ML at scale
• 📈 Demand: Very high (fastest growing ML role)

Skills You'll Demonstrate

✅ Kubernetes expertise with GPU scheduling ✅ End-to-end MLOps pipeline implementation ✅ LLM infrastructure and RAG systems ✅ Distributed training and GPU optimization ✅ Production monitoring and observability ✅ Cloud platform mastery (AWS, GCP, Azure) ✅ Infrastructure as Code with Terraform ✅ Cost optimization strategies

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📊 Repository Statistics

• Total Files: 207
• Estimated Lines: ~95,000+
• Modules: 10 (all complete)
• Projects: 3 (all complete)
• Learning Hours: 500+
• Technologies: 50+

Technology Stack Covered

Core Infrastructure: Docker, Kubernetes, Terraform, Helm, ArgoCD

ML & Data: PyTorch, TensorFlow, Apache Airflow, Apache Spark, Kafka, DVC

MLOps: MLflow, Feature Stores, Model Registry, CI/CD

LLM Infrastructure: vLLM, TensorRT-LLM, LangChain, Vector Databases (Pinecone, Milvus, ChromaDB)

Cloud Platforms: AWS (EC2, S3, EKS, SageMaker), GCP (GCE, GCS, GKE, Vertex AI), Azure (VMs, AKS, Azure ML)

Monitoring: Prometheus, Grafana, OpenTelemetry, Jaeger, ELK Stack

GPU Computing: CUDA, NCCL, Multi-GPU training, Distributed training

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for:

• Bug reports and fixes
• Documentation improvements
• New exercises and examples
• Updated best practices

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🆘 Getting Help

• 📖 Documentation: Start with GETTING_STARTED.md
• 💬 GitHub Discussions: Ask questions
• 🐛 Issues: Report bugs
• 📧 Contact: [email protected]

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📜 License

This project is licensed under the MIT License - see LICENSE for details.

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🌟 Success Metrics

Upon completion, you should be able to:

• Deploy ML models to production with confidence
• Build complete MLOps pipelines from scratch
• Implement LLM infrastructure with RAG
• Optimize cloud costs by 60-80%
• Debug complex distributed systems
• Pass technical interviews for AI Infrastructure roles
• Confidently discuss trade-offs in system design
• Lead infrastructure projects at your organization

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🚀 Next Steps After Completion

This curriculum prepares you for AI Infrastructure Engineer roles. For career progression:

1. Gain Experience (1-2 years)

   • Work on production ML systems
   • Handle incidents and on-call rotations
   • Contribute to open-source ML infrastructure projects

2. Advance to Senior Engineer (2-3 years total)

   • Continue with the Senior Engineer track
   • Lead larger projects and mentor juniors
   • Design complex systems

3. Become an Architect (4-6 years total)

   • Continue with the Architect track
   • Design enterprise ML platforms
   • Strategic technical leadership

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Ready to Master AI Infrastructure Engineering?

Start your journey today!

📘 Get Started | 📚 View Full Curriculum | 🚀 Start Module 01

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Share with others learning AI Infrastructure Engineering!

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Contact: [email protected]

Happy Learning! 🎓🚀

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