ML Model Development Template

A production-ready template for developing machine learning models that can be deployed to web (Laravel/React) and mobile (React Native) applications. This template provides a standardized structure and workflow for training models and exporting them for various deployment targets.

Features

• Structured Development Workflow: From data exploration to production deployment
• Multiple Export Formats:
  • TensorFlow.js for React/React Native
  • TensorFlow Lite for mobile
  • TensorFlow Serving for REST APIs
• Development Tools:
  • Jupyter notebooks for experimentation
  • Type checking and linting
  • Automated testing
  • Experiment tracking
• Production Ready:
  • Model versioning
  • Export utilities
  • Performance optimization

Project Structure

.
├── config/                            # Configuration files
├── data/                              # Data files
│   ├── processed/                     # Preprocessed data
│   └── raw/                           # Original data
├── logs/                              # Logs
│   ├── tensorboard/                   # Training logs   
│   └── app.log                        # Application logs
├── models/                            # Models
│   ├── checkpoints/                   # Training checkpoints
│   ├── exported/                      # Production-ready exports
│   │   ├── tfjs/                      # TensorFlow.js models
│   │   ├── tflite/                    # TensorFlow Lite models
│   │   └── serving/                   # TensorFlow Serving models
│   └── saved_models/                  # Saved TensorFlow models
│       └── {version}_{timestamp}/     # Models with version and timestamp
│           ├── metadata.json          # Model metadata
│           ├── model.keras            # Keras model
│           └── model.h5               # Saved model
├── notebooks/                         # Jupyter notebooks
├── src/                               # Source code
│   └── tools/                         # Continuous Integration configs

Setup

1. Prerequisites

   # Install Python 3.11+
   python --version  # Should be 3.11 or higher
   
   # Install Poetry
   curl -sSL https://install.python-poetry.org | python3 -

2. Project Installation

   # Clone the repository
   git clone <repository-url>
   cd licence-plate-detection
   
   # Install dependencies
   poetry install
   
   # Activate the virtual environment
   poetry shell

3. Environment Setup

   # Run setup script
   ./setup.sh

Development Workflow

1. Data Exploration and Model Development

Use the provided Jupyter notebooks in notebooks/:

• 1_data_exploration.ipynb: Data analysis and preprocessing
• 2_model_development.ipynb: Model architecture and training
• 3_model_export.ipynb: Export models for production

2. Training

# Train a model
poetry run ml train --settings settings/default.py

# Monitor data
tensorboard --logdir logs/tensorboard

3. Export for Production

React/React Native (TensorFlow.js)

# Export model
poetry run ml export --format tfjs --output models/exported/tfjs

# Usage in React:
import * as tf from '@tensorflow/tfjs';

const model = await tf.loadLayersModel('path/to/model.json');
const prediction = model.predict(tf.tensor(data));

Mobile (TensorFlow Lite)

# Export model
poetry run ml export --format tflite --output models/exported/tflite

# The exported model can be used with TensorFlow Lite in mobile apps

Laravel Backend (TensorFlow Serving)

# Export model
poetry run ml export --format serving --output models/exported/serving

# Start TensorFlow Serving
docker run -p 8501:8501 \\
    --mount type=bind,source=/path/to/models/exported/serving,target=/models/my_model \\
    -e MODEL_NAME=my_model -t tensorflow/serving

# Make predictions via REST API:
curl -X POST http://localhost:8501/v1/models/my_model/predict \\
    -d '{"instances": [[1.0, 2.0, 3.0]]}'

Development Tools

# Run all quality checks
poetry run lint

# Run tests
poetry run test

# Clean project
poetry run clean

Best Practices

1. Version Control

   • Use semantic versioning for models
   • Tag releases when exporting to production
   • Document model changes

2. Testing

   • Write unit tests for preprocessing
   • Test model inputs/outputs
   • Validate exported models

3. Deployment

   • Monitor model performance
   • Version API endpoints
   • Implement gradual rollout

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and quality checks
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.