This project focuses on building an end-to-end MLOps pipeline for deploying machine learning models into production environments. It manages the complete lifecycle from data ingestion and model training to automated deployment and monitoring. CI/CD pipelines are implemented to validate code, data, and models before release. Model performance, data drift, and system health are continuously tracked using monitoring tools. The project reflects real-world MLOps practices used by enterprise AI teams to ensure scalable, reliable, and repeatable ML deployments.

This project focuses on developing a scalable recommendation system using MLOps best practices. It handles the full workflow from data versioning and feature engineering to model training and containerized deployment. Automated CI/CD pipelines ensure seamless model updates with minimal downtime. Kubernetes is used to scale inference services based on demand, while monitoring tools track accuracy and latency. The project represents production-grade recommendation systems commonly used in e-commerce and media platforms.

This project focuses on designing an intelligent MLOps framework to detect data drift and trigger automated model retraining. It monitors incoming data distributions and compares them against baseline training data to identify performance degradation. Business rules are applied to decide retraining thresholds and validation steps. The system automatically deploys updated models after successful evaluation. The project mirrors real-world AI governance workflows used by organizations to maintain model accuracy in dynamic environments.