This repository contains the complete workflow of an end-to-end machine learning project, encompassing data ingestion, transformation, model training, evaluation, and deployment. The project also includes setting up CI/CD pipelines using GitHub Actions and deployment on AWS.
The project is structured as follows:
- Data Ingestion: Scripts and documentation related to data collection or generation.
- Data Transformation: Code for data cleaning, preprocessing, and transformation to make it suitable for modeling.
- Model Trainer: Scripts to train the machine learning model.
- Model Evaluation: Code to evaluate the model performance using various metrics.
- Model Deployment: Instructions and scripts for deploying the model into a production environment.
We use GitHub Actions for continuous integration and deployment, ensuring our codebase is robust and deployable at any moment.
The model is deployed to AWS, utilizing services such as AWS Lambda, Amazon S3, and Amazon EC2 to host and serve the model predictions.