This project serves as a comprehensive guide to constructing an end-to-end machine learning architecture suitable for deployment on a server—whether local or cloud-based.
The goal is to provide a clear exposition of the typical workflow in production-level projects, including but not limited to:
- Modular Coding: Crafting code in a manner that promotes readability and reusability.
- Logging: Ensuring that all actions and transactions are properly recorded.
- Model Preservation: Techniques for saving and retrieving machine learning models efficiently.
- Pipeline Construction: Establishing a streamlined process for predictions, allowing for direct input into predefined fields on a frontend interface.
The culmination of this project is the deployment phase, where the machine learning model is integrated with a frontend website, facilitating user interaction and prediction generation through a user-friendly interface.