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Full Stack Machine Learning Deployment with MLOps

  • Unique Paper ID: 170243
  • Volume: 11
  • Issue: 6
  • PageNo: 3696-3707
  • Abstract:
  • The successful deployment of machine learning (ML) models into production environments often faces significant challenges due to the complexity of workflows, the need for seamless integration, and the demand for scalability. This research presents a comprehensive approach to addressing these challenges by integrating Machine Learning Operations (MLOps) principles with the computational power and flexibility of AWS EC2. The proposed system delivers a full-stack ML pipeline for wine quality prediction, encompassing data ingestion, validation, preprocessing, model training, evaluation, and deployment within an automated, end-to-end workflow. Key features of the pipeline include modular architecture with configuration management facilitated by YAML files, ensuring adaptability to evolving project requirements, and robust experiment tracking and model versioning via MLflow, enabling reproducibility and traceability throughout the ML lifecycle. By implementing Continuous Integration and Continuous Deployment (CI/CD) practices, the pipeline reduces manual intervention and enhances operational efficiency. The study addresses critical challenges such as data quality assurance, efficient resource utilization, and real-time model monitoring. Deployment on AWS EC2 provides the scalability required for large-scale data processing, ensuring the pipeline’s readiness for real-world applications. Detailed insights into the system’s design, implementation, and optimization underscore the practicality of MLOps in bridging the gap between theoretical concepts and production-ready ML systems. This research contributes a scalable, flexible, and efficient framework for building and operationalizing ML workflows, offering actionable strategies for future developments in the field.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 3696-3707

Full Stack Machine Learning Deployment with MLOps

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