🤖 Ibm Ai Engineering Capstone

IBM AI Engineering Professional Certificate Capstone Project - Deep learning and computer vision platform

English | Português

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English

🎯 Overview

Ibm Ai Engineering Capstone is a production-grade Python application that showcases modern software engineering practices including clean architecture, comprehensive testing, containerized deployment, and CI/CD readiness.

The codebase comprises 1,402 lines of source code organized across 5 modules, following industry best practices for maintainability, scalability, and code quality.

✨ Key Features

• 🤖 ML Pipeline: End-to-end machine learning workflow from data to deployment
• 🔬 Feature Engineering: Automated feature extraction and transformation
• 📊 Model Evaluation: Comprehensive metrics and cross-validation
• 🚀 Model Serving: Production-ready prediction API
• 🏗️ Object-Oriented: 4 core classes with clean architecture

🏗️ Architecture

graph TB
    subgraph Client["🖥️ Client Layer"]
        A[REST API Client]
        B[Swagger UI]
    end
    
    subgraph API["⚡ API Layer"]
        C[Authentication & Rate Limiting]
        D[Request Validation]
        E[API Endpoints]
    end
    
    subgraph ML["🤖 ML Engine"]
        F[Feature Engineering]
        G[Model Training]
        H[Prediction Service]
        I[Model Registry]
    end
    
    subgraph Data["💾 Data Layer"]
        J[(Database)]
        K[Cache Layer]
        L[Data Pipeline]
    end
    
    A --> C
    B --> C
    C --> D --> E
    E --> H
    E --> J
    H --> F --> G
    G --> I
    I --> H
    E --> K
    L --> J
    
    style Client fill:#e1f5fe
    style API fill:#f3e5f5
    style ML fill:#e8f5e9
    style Data fill:#fff3e0

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classDiagram
    class PerformanceTest
    class AIEngineeringPlatform
    class ComputerVisionEngine
    AIEngineeringPlatform --> PerformanceTest : uses
    AIEngineeringPlatform --> AIEngineeringPlatform : uses
    AIEngineeringPlatform --> ComputerVisionEngine : uses

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🚀 Quick Start

Prerequisites

• Python 3.12+
• pip (Python package manager)

Installation

# Clone the repository
git clone https://github.com/galafis/ibm-ai-engineering-capstone.git
cd ibm-ai-engineering-capstone

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running

# Run the application
python src/main.py

🧪 Testing

# Run all tests
pytest

# Run with coverage report
pytest --cov --cov-report=html

# Run specific test module
pytest tests/test_main.py -v

# Run with detailed output
pytest -v --tb=short

📁 Project Structure

ibm-ai-engineering-capstone/
├── assets/
├── src/          # Source code
│   ├── ai_platform.py
│   └── main_platform.py
├── tests/         # Test suite
│   ├── __init__.py
│   ├── performance_test.py
│   └── test_platform.py
├── LICENSE
├── README.md
└── requirements.txt

🛠️ Tech Stack

Technology	Description	Role
Python	Core Language	Primary
Flask	Lightweight web framework	Framework
NumPy	Numerical computing	Framework
Pandas	Data manipulation library	Framework
scikit-learn	Machine learning library	Framework
TensorFlow	Deep learning framework	Framework

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

👤 Author

Gabriel Demetrios Lafis

• GitHub: @galafis
• LinkedIn: Gabriel Demetrios Lafis

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Português

🎯 Visão Geral

Ibm Ai Engineering Capstone é uma aplicação Python de nível profissional que demonstra práticas modernas de engenharia de software, incluindo arquitetura limpa, testes abrangentes, implantação containerizada e prontidão para CI/CD.

A base de código compreende 1,402 linhas de código-fonte organizadas em 5 módulos, seguindo as melhores práticas do setor para manutenibilidade, escalabilidade e qualidade de código.

✨ Funcionalidades Principais

• 🤖 ML Pipeline: End-to-end machine learning workflow from data to deployment
• 🔬 Feature Engineering: Automated feature extraction and transformation
• 📊 Model Evaluation: Comprehensive metrics and cross-validation
• 🚀 Model Serving: Production-ready prediction API
• 🏗️ Object-Oriented: 4 core classes with clean architecture

🏗️ Arquitetura

graph TB
    subgraph Client["🖥️ Client Layer"]
        A[REST API Client]
        B[Swagger UI]
    end
    
    subgraph API["⚡ API Layer"]
        C[Authentication & Rate Limiting]
        D[Request Validation]
        E[API Endpoints]
    end
    
    subgraph ML["🤖 ML Engine"]
        F[Feature Engineering]
        G[Model Training]
        H[Prediction Service]
        I[Model Registry]
    end
    
    subgraph Data["💾 Data Layer"]
        J[(Database)]
        K[Cache Layer]
        L[Data Pipeline]
    end
    
    A --> C
    B --> C
    C --> D --> E
    E --> H
    E --> J
    H --> F --> G
    G --> I
    I --> H
    E --> K
    L --> J
    
    style Client fill:#e1f5fe
    style API fill:#f3e5f5
    style ML fill:#e8f5e9
    style Data fill:#fff3e0

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🚀 Início Rápido

Prerequisites

• Python 3.12+
• pip (Python package manager)

Installation

# Clone the repository
git clone https://github.com/galafis/ibm-ai-engineering-capstone.git
cd ibm-ai-engineering-capstone

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running

# Run the application
python src/main.py

🧪 Testing

# Run all tests
pytest

# Run with coverage report
pytest --cov --cov-report=html

# Run specific test module
pytest tests/test_main.py -v

# Run with detailed output
pytest -v --tb=short

📁 Estrutura do Projeto

ibm-ai-engineering-capstone/
├── assets/
├── src/          # Source code
│   ├── ai_platform.py
│   └── main_platform.py
├── tests/         # Test suite
│   ├── __init__.py
│   ├── performance_test.py
│   └── test_platform.py
├── LICENSE
├── README.md
└── requirements.txt

🛠️ Stack Tecnológica

Tecnologia	Descrição	Papel
Python	Core Language	Primary
Flask	Lightweight web framework	Framework
NumPy	Numerical computing	Framework
Pandas	Data manipulation library	Framework
scikit-learn	Machine learning library	Framework
TensorFlow	Deep learning framework	Framework

🤝 Contribuindo

Contribuições são bem-vindas! Sinta-se à vontade para enviar um Pull Request.

📄 Licença

Este projeto está licenciado sob a Licença MIT - veja o arquivo LICENSE para detalhes.

👤 Autor

Gabriel Demetrios Lafis

• GitHub: @galafis
• LinkedIn: Gabriel Demetrios Lafis