# AI/ML Application

A production-ready AI/ML application structure.

## Project Structure

```
├── src/
│   ├── models/          # Model architectures
│   ├── training/        # Training scripts
│   ├── inference/       # Prediction/inference code
│   ├── data/            # Data loading and processing
│   └── utils/           # Utility functions
├── notebooks/           # Experimentation notebooks
├── models_saved/        # Trained model checkpoints
├── config/              # Configuration files
├── tests/
├── requirements.txt
└── README.md
```

## Getting Started

1. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

2. Train a model:
   ```bash
   python src/training/train.py
   ```

3. Run inference:
   ```bash
   python src/inference/predict.py
   ```

## Best Practices

- Version your models
- Track experiments (use MLflow/Weights&Biases)
- Separate training and inference code
- Use configuration files for hyperparameters
- Monitor model performance
