# Module 20: Capstone - Complete ML System Integration

## Overview
Combine everything you've learned to build a complete, optimized ML system from scratch. This is your masterpiece - demonstrating mastery of both ML algorithms and systems engineering.

## Project Options

### Option 1: Optimized CIFAR-10 Trainer
**Goal**: 75% accuracy with minimal resources
- Start with your Module 10 trainer
- Apply all optimizations (acceleration, quantization, pruning)
- Achieve same accuracy with 10x less compute/memory
- Deploy on resource-constrained device

### Option 2: Efficient GPT Inference Engine
**Goal**: Real-time text generation on CPU
- Implement KV caching for transformers
- Quantize model to INT8
- Optimize attention computation
- Generate 100 tokens/second on laptop CPU

### Option 3: Custom Challenge
**Goal**: Define your own optimization challenge
- Pick a problem you care about
- Set performance targets
- Apply systematic optimization
- Document the journey

## What You'll Demonstrate

### 1. Full Stack Understanding
- Build complete training pipeline
- Implement model architecture
- Add optimization layers
- Deploy to production

### 2. Systems Engineering
- Profile and identify bottlenecks
- Apply appropriate optimizations
- Measure and validate improvements
- Handle resource constraints

### 3. Scientific Approach
- Baseline measurements
- Systematic optimization
- Ablation studies
- Reproducible results

## Capstone Structure

### Week 1: Planning & Baseline
```python
# 1. Choose project and define success metrics
metrics = {
    'accuracy_target': 75.0,
    'inference_time': '<10ms',
    'memory_usage': '<100MB',
    'model_size': '<10MB'
}

# 2. Build baseline system
baseline = build_baseline_model()
baseline_metrics = evaluate(baseline)

# 3. Profile and identify opportunities
bottlenecks = profile_system(baseline)
```

### Week 2: Optimization Sprint
```python
# 4. Apply optimizations systematically
optimized = baseline
optimized = apply_acceleration(optimized)
optimized = apply_quantization(optimized)  
optimized = apply_pruning(optimized)
optimized = apply_caching(optimized)

# 5. Measure improvements
for optimization in optimizations:
    metrics = evaluate(optimized)
    speedup = baseline_time / optimized_time
    print(f"{optimization}: {speedup}x faster")
```

### Week 3: Polish & Deploy
```python
# 6. Final optimization pass
final_model = fine_tune_optimizations(optimized)

# 7. Create deployment package
deployment = package_for_production(final_model)

# 8. Document results
write_technical_report(baseline, final_model, metrics)
```

## Deliverables

### 1. Working System
- Complete codebase on GitHub
- README with setup instructions
- Demonstration video/notebook

### 2. Technical Report
- Problem statement and approach
- Baseline vs optimized metrics
- Optimization journey and decisions
- Lessons learned

### 3. Performance Analysis
- Comprehensive benchmarks
- Ablation study results
- Resource utilization graphs
- Comparison with PyTorch/TensorFlow

## Evaluation Criteria

### Technical Excellence (40%)
- Correctness of implementation
- Quality of optimizations
- Code organization and style

### Performance Achievement (30%)
- Meeting stated goals
- Improvement over baseline
- Resource efficiency

### Systems Understanding (30%)
- Appropriate optimization choices
- Understanding of tradeoffs
- Scientific methodology

## Example Projects from Past Students

### "TinyYOLO" - Real-time Object Detection
- 30 FPS on Raspberry Pi
- 90% size reduction through pruning
- Custom INT8 kernels for ARM

### "NanoGPT" - Edge Language Model
- 100MB model generates Shakespeare
- KV caching + quantization
- Runs on 2015 laptop

### "SwiftCNN" - Instant Image Classification
- <1ms inference on iPhone
- Structured pruning + iOS Metal
- 95% of ResNet accuracy at 10% size

## Resources
- All previous module code
- TinyTorch optimization library
- Benchmarking tools
- Community Discord for help

## Success Criteria
- ✅ Complete working system with all optimizations
- ✅ 10x+ improvement in speed OR memory
- ✅ Professional documentation and analysis
- ✅ Understanding of when/why to apply each optimization
- ✅ Ready for ML systems engineering roles!

## Final Note
This is your chance to show everything you've learned. Build something you're proud of - something that demonstrates not just that you can implement ML algorithms, but that you understand how to build production ML systems.

**Remember**: The goal isn't perfection, it's demonstrating systematic thinking about performance, memory, and deployment constraints - the real challenges of ML engineering.