# AI/ML Project

**Rhodium Level Compliance Example (Aspirational)**

[![Rhodium Compliance](https://img.shields.io/badge/rhodium-rhodium-9C27B0)](https://github.com/Hyperpolymath/rhodium-standard-repositories)
image:https://img.shields.io/badge/License-MPL_2.0-blue.svg[MPL-2.0-or-later,link="https://opensource.org/licenses/MPL-2.0"]

A comprehensive AI/ML project demonstrating **Rhodium** level compliance - the highest tier of the Rhodium Framework. This example showcases:

- Multi-language support (Rust + Python)
- ML model development and deployment
- Automated compliance checking
- Complete observability and monitoring
- AI/ML specific best practices
- Full DevOps automation

## Features

### Multi-Language Architecture

- **Rust**: High-performance inference engine and API server
- **Python**: Model training, data processing, and experimentation
- **Polyglot Integration**: Seamless Rust-Python interop via PyO3

### Machine Learning Pipeline

1. **Data Processing**: Python scripts for data preparation
2. **Training**: Model training with experiment tracking
3. **Inference**: Rust-based high-performance inference engine
4. **Serving**: REST API for model serving
5. **Monitoring**: Model performance monitoring

### AI/ML Specific Features

- Model versioning and registry
- Experiment tracking
- Data validation
- Model validation
- A/B testing support
- Model explainability
- Bias detection
- Performance benchmarking

## Quick Start

```bash
# Setup Python environment
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Build Rust components
cargo build --release

# Run tests
make test

# Train model
python python/train.py

# Start inference server
cargo run --bin inference-server

# Check Rhodium compliance
rhodium check --level rhodium
```

## Project Structure

```
ai-ml-project/
├── src/              # Rust inference engine
├── python/           # Python ML code
├── models/           # Trained models
├── data/            # Dataset storage
├── notebooks/       # Jupyter notebooks
├── tests/           # All tests
├── scripts/         # Automation scripts
├── .rhodium/        # Rhodium config
└── docs/            # Documentation
```

## Rhodium Level Compliance

This project demonstrates **Rhodium** level - the highest compliance tier:

```bash
rhodium check --level rhodium --strict
```

### All Compliance Levels Met

#### Bronze Level ✓
- README, LICENSE, CODE_OF_CONDUCT
- Basic tests
- Version control

#### Silver Level ✓
- CHANGELOG, CONTRIBUTING, CITATION
- Issue/PR templates
- >70% test coverage
- API documentation

#### Gold Level ✓
- ARCHITECTURE.md, SECURITY.md
- ADRs
- SBOM, provenance tracking
- Performance benchmarks
- CI/CD pipelines

#### Rhodium Level ✓
- Multi-language support
- Advanced automation
- ML-specific governance
- Complete observability
- Automated compliance checks
- Model cards
- Dataset cards
- Fairness assessment
- Carbon footprint tracking

## ML-Specific Documentation

- [MODEL_CARD.md](MODEL_CARD.md) - Model documentation
- [DATA_CARD.md](DATA_CARD.md) - Dataset documentation
- [FAIRNESS.md](FAIRNESS.md) - Bias and fairness assessment
- [docs/ml-pipeline.md](docs/ml-pipeline.md) - ML pipeline documentation

## Development

### Training a Model

```bash
# Prepare data
python python/prepare_data.py

# Train model
python python/train.py --config configs/model_config.yaml

# Evaluate model
python python/evaluate.py --model-path models/latest
```

### Inference

```bash
# Start server
cargo run --bin inference-server

# Make prediction
curl -X POST http://localhost:8080/predict \
  -H "Content-Type: application/json" \
  -d '{"features": [1.0, 2.0, 3.0]}'
```

### Testing

```bash
# All tests
make test

# Rust tests
cargo test

# Python tests
pytest python/tests/

# Integration tests
./scripts/integration_tests.sh
```

## Monitoring

- **Metrics**: Prometheus metrics endpoint
- **Tracing**: Distributed tracing with Jaeger
- **Logging**: Structured logging
- **Model Metrics**: Prediction accuracy, latency, drift detection

## Security

See [SECURITY.md](SECURITY.md) for:
- Model security
- Data privacy
- Adversarial robustness
- Supply chain security

## Compliance Automation

Automated checks in CI/CD:
- Code quality (Rust: clippy, Python: ruff)
- Test coverage (>80%)
- Security scanning
- License compliance
- Documentation completeness
- Model validation
- Data validation
- Fairness metrics

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for development guidelines.

## License

Licensed under MIT OR Apache-2.0.

## Citation

```bibtex
@software{ai_ml_project,
  title = {AI/ML Project: Rhodium Compliance Example},
  author = {Rhodium Framework Contributors},
  year = {2025},
  url = {https://github.com/Hyperpolymath/rhodium-standard-repositories}
}
```

See [CITATION.cff](CITATION.cff) for more details.
