LLM Serving & Inference: Complete Learning Repository

A comprehensive, hands-on guide to learning LLM serving and inference from fundamentals to production deployment.

🎯 What You’ll Learn

This repository teaches you everything about serving Large Language Models in production:

• How LLM inference works (tokenization, attention, generation)
• Basic serving (FastAPI, model loading, endpoints)
• Containerization (Docker for ML workloads)
• Kubernetes deployment (production infrastructure)
• Performance optimization (vLLM, batching, GPU utilization)
• Scaling (autoscaling, load balancing)
• Production practices (monitoring, versioning, drift detection)

📁 Repository Structure

mlops_serving/
├── LEARNING_PATH.md          # Start here! Complete learning guide
├── docs/                     # Detailed concept explanations
│   ├── llm_inference_fundamentals.md
│   ├── serving_architectures.md
│   └── optimization_techniques.md
├── 01_basic_serving/         # FastAPI + HuggingFace serving
├── 02_docker/                # Containerization
├── 03_kubernetes/            # K8s deployment
├── 04_load_testing/          # Performance testing
├── 05_vllm_serving/          # High-performance serving
├── 06_autoscaling/           # K8s autoscaling
├── 07_canary_deployments/    # Gradual rollouts
├── 08_monitoring/            # Prometheus + Grafana
├── 09_model_versioning/      # Version management
├── 10_drift_detection/       # Evidently integration
└── 11_triton/                # Multi-model serving

🚀 Quick Start

👉 START HERE: Read HOW_TO_START.md for a complete step-by-step guide!

Quick Commands

# 1. Set up environment
cd 01_basic_serving
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# 2. Start the server
python app.py

# 3. Test it (in another terminal)
python test_api.py
# OR visit http://localhost:8000/docs for interactive API docs

📚 Learning Approach

This is a hands-on, topic-based learning path:

1. Each topic is self-contained with code, docs, and exercises
2. Build incrementally - each topic builds on previous concepts
3. Learn by doing - run code, modify it, break it, fix it
4. Focus on understanding - not just copying code

🎓 Prerequisites

• Python 3.9+
• Basic Python knowledge
• Understanding of REST APIs
• Docker (for containerization topics)
• Kubernetes cluster (minikube/kind for K8s topics)
• GPU (optional, helpful for vLLM)

📖 Topics Covered

Core Concepts

• LLM architecture and inference pipeline
• Tokenization and vocabulary
• Attention mechanisms and KV caching
• Memory and computation requirements

Serving Basics

• Model loading and initialization
• API endpoint design
• Request/response handling
• Error handling and validation

Infrastructure

• Docker containerization
• Kubernetes deployment
• Health checks and probes
• Resource management

Performance

• Load testing and benchmarking
• Latency vs throughput
• vLLM optimization
• GPU utilization

Production

• Autoscaling strategies
• Canary deployments
• Monitoring and observability
• Model versioning
• Drift detection

🔧 Technologies

• FastAPI: Web framework
• HuggingFace Transformers: Model loading
• vLLM: High-performance inference
• Docker: Containerization
• Kubernetes: Orchestration
• Locust: Load testing
• Prometheus: Metrics
• Grafana: Visualization
• Evidently: Drift detection
• Triton: Multi-model serving

📝 How to Use This Repository

1. Start with LEARNING_PATH.md - Understand the structure
2. Read the fundamentals - docs/llm_inference_fundamentals.md
3. Work through topics in order - Each numbered topic builds on the last
4. Experiment and modify - Don’t just run code, change it and learn
5. Read the code comments - They explain the “why” not just the “what”
6. Check solutions - See EXERCISE_SOLUTIONS.md for exercise answers
7. Prepare for interviews - Review INTERVIEW_QA.md for common questions

🎯 Learning Goals

By completing this repository, you’ll be able to:

• Understand how LLM inference works internally
• Build production-ready serving APIs
• Deploy models to Kubernetes
• Optimize for performance and cost
• Monitor and debug serving systems
• Handle model updates and drift

🤝 Contributing

This is a learning repository. Feel free to:

• Add more examples
• Improve documentation
• Fix bugs
• Share your learnings

📄 License

MIT License - Feel free to use this for learning and teaching.

📖 Getting Started

1. New to this? → Read HOW_TO_START.md for step-by-step instructions
2. Want overview? → Read LEARNING_PATH.md to see all topics
3. Ready to code? → Start with 01_basic_serving/
4. Need help? → Check EXERCISE_SOLUTIONS.md for exercise answers
5. Interview prep? → Review INTERVIEW_QA.md for common questions

Ready to start? Open HOW_TO_START.md and begin your journey! 🚀

📚 Additional Resources

• Exercise Solutions: EXERCISE_SOLUTIONS.md - Answers to all exercises
• Interview Q&A: INTERVIEW_QA.md - 23+ interview questions with detailed answers
• Quick Reference: QUICK_REFERENCE.md - Quick commands and tips
• Complete Topics: COMPLETE_TOPICS.md - Overview of all 12 topics
• Industry Boilerplate: Each topic README includes industry-standard code and use cases

🏭 Industry Focus

Each topic now includes:

• Why We Need This: Business and technical justification
• Industry Use Cases: Real-world applications (5+ examples per topic)
• Boilerplate Code: Production-ready, industry-standard implementations
• Company Examples: How major companies use these technologies

Topics updated with industry content:

• ✅ Topic 1: Basic Serving (Customer support, content generation)
• ✅ Topic 2: Docker (Multi-cloud, CI/CD, edge deployment)
• ✅ Topic 3: Kubernetes (Large-scale platforms, enterprise ML)
• ✅ Topic 5: vLLM (High-throughput APIs, cost optimization)
• ✅ Topic 8: Monitoring (Production observability, incident response)