The End-to-End Recommender System is a machine learning-based application designed to recommend books to users based on collaborative filtering. The project encompasses a complete MLOps pipeline, including data ingestion, validation, transformation, model training, and a web-based user interface for interaction.

This project showcases a complete, end-to-end book recommender system, demonstrating the entire MLOps lifecycle. It covers data processing, model training, evaluation, and deployment. With a modular codebase and pipeline automation, this repository highlights practical skills in building and deploying production-ready ML applications.