MLOPS-Pipeline-Dashboard 🚀

Welcome to the MLOPS-Pipeline-Dashboard repository! This project provides an enterprise-ready MLOps dashboard that simplifies the machine learning workflow for business analysts and non-technical users. With just four simple steps, you can upload your data, train a model, deploy it, and make predictions.

Table of Contents

• Features
• Technologies Used
• Getting Started
• Usage
• Folder Structure
• Automated Testing
• Documentation
• Deployment Guides
• Contributing
• License
• Contact
• Releases

Features

• User-Friendly Interface: Designed for business analysts and non-technical users.
• Four-Step ML Pipeline:
  1. Upload CSV
  2. Train Model
  3. Deploy
  4. Predict
• Automated Testing: Ensures the reliability of your models and pipeline.
• Comprehensive Documentation: Guides you through every step of the process.
• Production Deployment Guides: Learn how to deploy your models in a production environment.

Technologies Used

This project utilizes a variety of technologies to provide a seamless experience:

• FastAPI: For building the web application.
• Python: The primary programming language for data science and machine learning tasks.
• CI/CD Tools: For automation of testing and deployment.
• No-Code Solutions: To make machine learning accessible to all users.
• Data Science Libraries: Such as Pandas, Scikit-learn, and others for data manipulation and model training.

Getting Started

To get started with the MLOPS-Pipeline-Dashboard, follow these steps:

1. Clone the Repository:

   git clone https://github.com/gituser2610/MLOPS-Pipeline-Dashboard.git
   cd MLOPS-Pipeline-Dashboard

2. Install Dependencies: Make sure you have Python installed. Then, install the required packages:

   pip install -r requirements.txt

3. Run the Application: Start the FastAPI server:

   uvicorn main:app --reload

4. Access the Dashboard: Open your browser and navigate to http://127.0.0.1:8000.

Usage

Once the application is running, you can start using the dashboard. Follow these steps:

1. Upload Your CSV: Use the upload button to select your CSV file.
2. Train Your Model: Click the "Train" button to start the model training process.
3. Deploy the Model: Once training is complete, deploy your model with a single click.
4. Make Predictions: Enter new data and click "Predict" to see results.

Folder Structure

Here's an overview of the project structure:

MLOPS-Pipeline-Dashboard/
│
├── app/
│   ├── main.py          # Entry point for the FastAPI application
│   ├── models.py        # Machine learning models
│   ├── utils.py         # Utility functions
│   ├── templates/       # HTML templates for the dashboard
│   └── static/          # Static files (CSS, JS)
│
├── tests/               # Automated tests
│
├── requirements.txt      # Python dependencies
│
└── README.md            # Project documentation

Automated Testing

Automated testing is crucial for ensuring the reliability of your machine learning models and the overall pipeline. This project includes a suite of tests located in the tests/ directory. To run the tests, use the following command:

pytest tests/

Documentation

Comprehensive documentation is available to help you navigate the features of the MLOPS-Pipeline-Dashboard. You can find detailed explanations of each component and step in the documentation files located in the docs/ directory.

Deployment Guides

To deploy your models in a production environment, refer to the deployment guides provided in the docs/ directory. These guides cover:

• Setting up a production server
• Configuring environment variables
• Using Docker for containerization
• Monitoring and maintaining your deployed models

Contributing

We welcome contributions to the MLOPS-Pipeline-Dashboard! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

License

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

Contact

For any inquiries or support, please contact the project maintainer at [[email protected]].

Releases

You can find the latest releases and download the necessary files from the Releases section. Make sure to download and execute the appropriate files for your needs.

For more information on updates and new features, check the Releases page regularly.

Thank you for your interest in the MLOPS-Pipeline-Dashboard! We hope you find it useful in your machine learning projects.