ML Workflow

Overview

ML Workflow is a comprehensive machine learning project designed to streamline the end-to-end process of data ingestion, preprocessing, modeling, evaluation, and deployment. It provides a robust framework for automating repetitive tasks in machine learning workflows.

Project Structure

The project structure is organized as follows:

• .ebextensions: Contains configuration files for Elastic Beanstalk deployment.
• artifact: Contains files related to hyperparameter tuning.
• catboost_info: Contains information related to CatBoost model tuning.
• notebook: Contains a Python file for data ingestion.
• src: Source code directory:
  • application.py: Updated application logic.
• templates: Contains front-end templates for user interaction.
• .DS_Store: System file (ignored).
• .gitignore: Specifies files and directories to be ignored by Git.
• README.md: This file, providing an overview of the project.
• requirements.txt: Lists project dependencies.
• setup.py: Package setup file.
• Dockerfile: Docker configuration file.

Setup and Installation

To set up the project locally:

1. Clone the repository: git clone <repository-url>
2. Navigate to the project directory: cd ml-workflow
3. Install dependencies: pip install -r requirements.txt
4. Run the application: python src/application.py

Usage

1. Ensure data is available in specified sources.
2. Run the data ingestion script to fetch the data.
3. Preprocess the data using provided scripts.
4. Train and evaluate models using the updated application logic.
5. Deploy trained models for real-world use.

Docker Setup

To containerize the project using Docker:

1. Ensure Docker is installed on your machine. You can download it from Docker's official website.

2. Build the Docker image:

   docker build -t <imagename>.

3. Run the Docker container:

   docker run -p 5000:5000 <imagename>

4. Access the application in your web browser at http://localhost:5000.

Configuration

Ensure all necessary configurations are set up correctly:

• Set environment configurations for deployment.
• Provide data source credentials.
• Adjust model training parameters as needed.

Contributing

Contributions to the project are welcome! To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/my-feature
3. Commit your changes: git commit -am 'Add new feature'
4. Push to the branch: git push origin feature/my-feature
5. Submit a pull request.

References

krish Naik: https://github.com/krishnaik06/mlproject.git

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