Complete Learning Topics Overview

All 12 learning topics are now complete! Here’s what you have:

✅ Completed Topics

1. LLM Inference Fundamentals ✅

• Location: docs/llm_inference_fundamentals.md
• What it covers: Tokenization, attention, generation, memory, computation
• Status: Complete with detailed explanations

2. Basic Model Serving ✅

• Location: 01_basic_serving/
• What it covers: FastAPI, HuggingFace, model loading, endpoints
• Status: Complete with working code

3. Containerization ✅

• Location: 02_docker/
• What it covers: Docker basics, multi-stage builds, GPU support
• Status: Complete with Dockerfiles and examples

4. Kubernetes Deployment ✅

• Location: 03_kubernetes/
• What it covers: K8s manifests, health checks, resources, ConfigMaps
• Status: Complete with deployment YAMLs

5. Load Testing & Performance ✅

• Location: 04_load_testing/
• What it covers: Latency measurement, Locust, performance profiling
• Status: Complete with test scripts

6. High-Performance Serving (vLLM) ✅

• Location: 05_vllm_serving/
• What it covers: vLLM setup, continuous batching, PagedAttention
• Status: Complete with server implementation

7. Autoscaling ✅

• Location: 06_autoscaling/
• What it covers: HPA, CPU/memory scaling, custom metrics
• Status: Complete with HPA configurations

8. Monitoring & Observability ✅

• Location: 08_monitoring/
• What it covers: Prometheus, Grafana, GPU monitoring, metrics
• Status: Complete with full monitoring stack

9. Canary Deployments ✅

• Location: 09_canary_deployments/
• What it covers: Traffic splitting, gradual rollouts, rollback
• Status: Complete with deployment examples

10. Model Versioning ✅

• Location: 10_model_versioning/
• What it covers: Version management, model registry, rollback
• Status: Complete with strategies and examples

11. Drift Detection ✅

• Location: 11_drift_detection/
• What it covers: Data drift, concept drift, Evidently AI
• Status: Complete with detector implementation

12. Multi-Model Serving (Triton) ✅

• Location: 12_triton/
• What it covers: Triton server, dynamic batching, model ensembles
• Status: Complete with configuration examples

📚 Learning Path

Follow this order for best learning experience:

1. Start: Read HOW_TO_START.md
2. Fundamentals: Read docs/llm_inference_fundamentals.md
3. Basic Serving: Work through 01_basic_serving/
4. Containerize: Learn Docker in 02_docker/
5. Deploy: Deploy to K8s with 03_kubernetes/
6. Test: Measure performance with 04_load_testing/
7. Optimize: Use vLLM in 05_vllm_serving/
8. Scale: Set up autoscaling in 06_autoscaling/
9. Monitor: Add monitoring in 08_monitoring/
10. Deploy Safely: Learn canary in 09_canary_deployments/
11. Version: Manage versions in 10_model_versioning/
12. Detect Issues: Add drift detection in 11_drift_detection/
13. Advanced: Use Triton in 12_triton/

🎯 Quick Reference

• Quick Start: HOW_TO_START.md
• Learning Path: LEARNING_PATH.md
• Quick Commands: QUICK_REFERENCE.md
• Overview: README.md

📖 Each Topic Includes

• README.md: Detailed explanations and concepts
• Code examples: Working implementations
• Configuration files: YAMLs, Dockerfiles, etc.
• Exercises: Hands-on practice suggestions

🚀 Ready to Learn!

Everything is set up and ready. Start with HOW_TO_START.md and work through each topic at your own pace.

Happy learning! 🎓