End-to-End Deployment

This project demonstrates the implementation of a full CI/CD pipeline using GitHub Actions for a simple NLP project. It is designed to apply concepts from Software Engineering for Data Scientists and integrate various best practices for code quality and automation.

Features

• Pre-commit Hooks: Utilized to automatically format code and remove outputs from Jupyter notebooks before commits.
• GitHub Actions: Configured to run automated tests located in the tests folder on every push and pull request, ensuring code quality and stability.
• Docker Integration: The project includes a Dockerfile that exposes an API endpoint, allowing external access to the NLP model.

Goal

The main objective of this project is to practice and solidify concepts related to continuous integration, continuous deployment, and software engineering for data science projects.

Running the Project

To build and run the Docker container, follow these steps:

1. Make the Script Executable:

   chmod +x run.sh

2. Execute the Script:

   sudo ./run.sh

This will build the Docker image and run the container, exposing the API endpoint on the specified port in run.sh