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🤖 AutoML-MLOps

Empowering Your Machine Learning Workflow

MIT License Contributions Welcome

Your All-in-One Solution for Streamlined Model Development and Deployment

Features · Getting Started · Why AutoML-MLOps · Contributing

✨ Features

Feature Description
🚀 Automated Model Training Upload your dataset and let AutoML-MLOps handle the rest
📊 Interactive Dashboard Real-time monitoring of training progress and model performance
🎯 Smart Target Selection Automatic detection or manual selection of your target column
📈 Comprehensive Metrics In-depth model evaluation with detailed metrics and visualizations
💾 Efficient Model Management Easy comparison and download of trained models
👁️ Data Visualization Built-in CSV data preview and exploration tools

🚀 Getting Started

1️⃣ Upload Your Data

  • Select the "Choose File" button
  • Upload your CSV dataset
  • Verify data preview

2️⃣ Configure Your Model

  • Choose target column detection method:
    • Automatic detection
    • Manual selection
  • Customize training parameters

3️⃣ Train Your Model

  • Initiate training with one click
  • Monitor real-time progress
  • View live training metrics

4️⃣ Explore Results

  • Analyze comprehensive model metrics
  • Explore interactive visualizations
  • Review performance indicators

5️⃣ Deploy Your Model

  • Download trained model
  • Access model artifacts
  • Ready for production deployment

💡 Why AutoML-MLOps?

Benefit Description
⏱️ Save Time Automate repetitive tasks in the ML pipeline
📈 Improve Accuracy Leverage advanced algorithms for optimal model selection
🔍 Gain Insights Visualize your data and model performance like never before
🔄 Stay Flexible Suitable for both beginners and experienced data scientists

🛠️ Technology Stack

Frontend

  • React
  • Next.js
  • Tailwind CSS

Backend

  • Python
  • scikit-learn

Visualization

  • Recharts

👥 Contributing

We value and welcome contributions from the community! Here's how you can contribute:

  1. Fork the repository
  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

For major changes, please open an issue first to discuss what you would like to change.

📄 License

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