An End-to-End Machine Learning Framework for Automated Wine Quality Prediction

Deploying machine learning (ML) models in real-world production environments presents significant challenges due to workflow complexity, integration demands, and scalability requirements. This study proposes a comprehensive solution by integrating Machine Learning Operations (MLOps) practices with the computational scalability and flexibility of AWS EC2 infrastructure. The developed system establishes a complete end-to-end ML pipeline for wine quality prediction, encompassing data ingestion, validation, preprocessing, model training, evaluation, and deployment within an automated workflow. The pipeline is designed with a modular architecture, utilizing YAML-based configuration management to ensure flexibility and ease of adaptation to changing project requirements. It further integrates MLflow for systematic experiment tracking and model version control, thereby enhancing reproducibility, traceability, and governance across the ML lifecycle. Through the adoption of continuous integration and continuous deployment (CI/CD) methodologies, the framework reduces manual intervention while improving operational efficiency and reliability. The proposed approach addresses critical challenges such as maintaining data integrity, optimizing computational resources, and enabling continuous real-time model monitoring. Deployment on AWS EC2 ensures elastic scalability to manage large-scale datasets and supports robust performance in practical production settings. Detailed discussions on system architecture, implementation strategies, and optimization techniques demonstrate the effectiveness of MLOps in bridging the gap between research-oriented ML development and production-grade deployment. Overall, this work delivers a scalable, adaptable, and efficient framework for developing and operationalizing ML workflows, offering practical insights and strategic guidance for future advancements in MLOps-driven systems.