Welcome to the Spell Correction with MLOps repository! This project utilizes machine learning and MLOps practices to build a robust spell correction system. It employs Docker for containerization, GitHub Actions for CI/CD, and is deployed on Render for live hosting.
- Introduction
- Topics Covered
- Getting Started
- Live Demo
- Docker and CI/CD
- Deploy on Render
- Best Practices
- FAQ
- Troubleshooting
- Contributing
- Additional Resources
- Challenges Faced
- Lessons Learned
- Why I Created This Repository
- License
- Contact
This repository showcases a machine learning project aimed at developing a spell correction system using MLOps methodologies. The project integrates Docker for containerization, GitHub Actions for CI/CD, and is deployed on Render for a live demonstration of the spell correction capabilities.
- Spell Correction Algorithms: Implementing machine learning models to correct spelling errors.
- Data Preprocessing: Techniques for cleaning and preparing text data for training.
- Model Evaluation: Assessing the performance of spell correction algorithms.
- Deployment: Implementing the model as a web service using Flask.
- Docker: Containerizing the application for consistent deployment across environments.
- CI/CD: Automating tests and deployments with GitHub Actions.
- Render: Deploying the application on Render for live access.
To get started with this project, follow these steps:
-
Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Spell-Correction-with-MLOps.git
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Navigate to the project directory:
cd ML-Project-Spell-Correction-with-MLOps -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
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Open your browser and visit:
http://127.0.0.1:5000/
Check out the live version of the Spell Correction app here.
This project is containerized using Docker to ensure that the environment is consistent across different systems.
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Build the Docker image:
docker build -t spell-correction-app . -
Run the Docker container:
docker run -p 5000:5000 spell-correction-app
-
Visit the application:
http://127.0.0.1:5000/
This project uses GitHub Actions for continuous integration and deployment. Each commit triggers the following workflow:
- Linting and Testing: Automatically runs linting and tests to ensure code quality.
- Build and Deploy: Builds the Docker image and deploys the application to a cloud platform (e.g., Render, Heroku).
You can find the CI/CD workflow file in .github/workflows/ci-cd.yml.
To deploy this application on Render, follow these steps:
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Sign up for Render: Visit Render and sign up for an account.
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Create a new Web Service:
- Select "New Web Service" from your Render dashboard.
- Connect your GitHub repository.
- Select your desired branch (e.g.,
main) and set up the build and runtime settings.
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Deploy: Render will automatically build and deploy your application. Once the deployment is successful, your application will be live.
-
Access your live app: Your application will be accessible via a Render-generated URL.
Recommendations for maintaining and improving this project:
- Model Updating: Continuously retrain the model with new text data to improve accuracy.
- Container Security: Ensure the Docker container is secure and free from vulnerabilities.
- Error Handling: Implement comprehensive error handling in both the app and the CI/CD pipeline.
- Documentation: Keep the documentation up-to-date with the latest changes and improvements.
Q: What is the purpose of this project?
A: This project implements a spell correction system using machine learning to automatically correct spelling mistakes in text.
Q: How can I contribute to this repository?
A: Refer to the Contributing section for details on how to contribute.
Q: Can I deploy this app on cloud platforms?
A: Yes, you can deploy the Dockerized app on platforms such as Heroku, Render, or AWS.
Common issues and solutions:
-
Issue: Docker Container Not Running
Solution: Ensure that Docker is properly installed and the image was built successfully. -
Issue: CI/CD Pipeline Failing
Solution: Check the GitHub Actions logs for errors and ensure all tests pass locally before committing. -
Issue: Model Accuracy Low
Solution: Verify that the training data is preprocessed correctly and consider tuning the hyperparameters of the model.
Contributions are welcome! Here's how you can contribute:
-
Fork the repository.
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Create a new branch:
git checkout -b feature/new-feature
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Make your changes:
- Add features, fix bugs, or improve documentation.
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Commit your changes:
git commit -am 'Add a new feature or update' -
Push to the branch:
git push origin feature/new-feature
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Submit a pull request.
Explore these resources for more insights into Docker, CI/CD, and machine learning:
- Docker Official Documentation: docs.docker.com
- GitHub Actions Documentation: docs.github.com
- Render Documentation: render.com/docs
- Machine Learning Tutorials: Kaggle
Some challenges during development:
- Implementing a robust spell correction algorithm that performs well across diverse text inputs.
- Setting up Docker for seamless deployment across environments.
- Configuring the CI/CD pipeline to automate the testing and deployment process.
Key takeaways from this project:
- Hands-on experience with Docker for containerizing machine learning applications.
- Setting up CI/CD pipelines for automated testing and deployment.
- Importance of continuous model improvement and deployment best practices.
This repository was created to demonstrate the end-to-end process of developing, containerizing, and deploying a machine learning model for spell correction, focusing on using MLOps best practices.
This repository is licensed under the MIT License. See the LICENSE file for more details.
- Email: iconicemon01@gmail.com
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
Feel free to further adjust any sections as necessary, and let me know if you need any additional modifications or enhancements!