A Fully Automated Machine Learning Pipeline for Predicting the Quality of Wine

Implementing machine learning (ML) models in production settings can present numerous challenges, mainly due to the intricate nature of workflows, the need for smooth integration, and the demand for scalability. This study proposes a comprehensive strategy to address these issues by integrating Machine Learning Operations (MLOps) principles with the computational power and flexibility of AWS EC2. The suggested system provides a complete ML pipeline for predicting wine quality, encompassing phases such as data ingestion, validation, preprocessing, model training, evaluation, and deployment, all within an automated end-to-end workflow. Notable features of the pipeline include a modular architecture with configuration management supported by YAML files, which allows for adjustments to meet changing project requirements. Furthermore, it integrates strong experiment tracking and model version control via MLflow, enhancing reproducibility and traceability throughout the ML lifecycle. By implementing continuous integration and deployment (CI/CD) practices, the pipeline reduces manual intervention and increases operational efficiency. This research addresses critical challenges, such as ensuring data quality, optimizing resource use, and facilitating real-time model monitoring. Leveraging AWS EC2 for deployment provides the scalability needed to manage large datasets and ensures that the pipeline is prepared for real-world applications. Detailed insights into system design, implementation, and optimization underscore the practicality of MLOps in bridging theoretical concepts with production-ready ML systems. This research offers a scalable, flexible, and effective framework for developing and deploying ML workflows, along with practical strategies for future progress in the domain.