🚀 MLflow.ai - Intelligent Machine Learning Pipeline Platform

🌟 Production-grade ML Pipeline Platform | Built with Modern Tech Stack 🌟

🎯 About MLflow.ai

MLflow.ai is a cutting-edge machine learning pipeline platform that revolutionizes the way data scientists and ML engineers work. It provides an intuitive, end-to-end solution for data preprocessing, model training, and evaluation, all wrapped in a beautiful, modern interface.

🏗️ Architecture

graph LR
    A[Frontend - React] --> B[Firebase Hosting]
    C[API - Flask] --> D[Azure Cloud]
    A <--> C
    E[ML Models] --> D

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💻 Tech Stack

Frontend

• Framework: React 18.3 with TypeScript
• Build Tool: Vite
• Styling: TailwindCSS
• UI Components: Radix UI
• Charts: Recharts
• Code Highlighting: Prism.js
• State Management: React Hooks
• Deployment: Firebase Hosting

Backend

• API Framework: Flask (Python)
• Cloud Platform: Azure
• Database: Azure SQL
• ML Libraries:
  • NumPy
  • Pandas
  • Scikit-learn
  • TensorFlow

DevOps

• Frontend Hosting: Firebase
• Backend Hosting: Azure App Service
• CI/CD: GitHub Actions
• Monitoring: Azure Monitor

🔌 API Architecture

📊 Data Management

POST /api/load          # Load dataset
GET  /api/head?n=5     # Get first n rows
GET  /api/describe     # Statistical description
GET  /api/missing      # Missing values analysis

⚙️ Preprocessing

POST /api/preprocess/encode      # Encode categorical variables
POST /api/preprocess/scale       # Scale features
POST /api/preprocess/split       # Split dataset

🤖 Model Operations

POST /api/model/init      # Initialize model
POST /api/model/train     # Train model
POST /api/model/evaluate  # Evaluate model
POST /api/model/predict   # Make predictions

✨ Key Features

• 🎨 Beautiful UI/UX

  • Responsive design
  • Dark/Light mode
  • Interactive visualizations

• 🔄 Real-time Processing

  • Live data preprocessing
  • Real-time model training status
  • Interactive data exploration

• 🤖 Advanced ML Capabilities

  • Multiple algorithm support
  • Automated feature engineering
  • Model performance comparison

• 📊 Comprehensive Analytics

  • Interactive dashboards
  • Custom visualization options
  • Detailed model metrics

🚀 Quick Start

# Clone repository
git clone https://github.com/yourusername/mlflow-ai.git

# Install dependencies
npm install

# Start development server
npm run dev

# Build for production
npm run build

📂 Project Structure

mlflow-ai/
├── src/
│   ├── api/           # API integration
│   ├── components/    # React components
│   ├── hooks/         # Custom React hooks
│   ├── config/        # Configuration
│   └── utils/         # Utility functions
├── public/            # Static assets
└── backend/          # Flask API
    ├── api/          # API routes
    ├── models/       # ML models
    └── utils/        # Backend utilities

🔐 Environment Setup

# Frontend (.env)
VITE_GROQ_API_KEY=your_key
VITE_API_URL=your_api_url

# Backend (.env)
AZURE_CONNECTION_STRING=your_connection
FLASK_ENV=development

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📝 License

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

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Built with ❤️ by Rohit Kumar