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@article{184251,
        author = {Mrunali Jibhakate},
        title = {Student Performance Forecasting with MLOps},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {4179-4183},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184251},
        abstract = {The integration of Machine Learning Operations (MLOps) has become increasingly important for developing reliable, scalable, and automated machine learning applications. This research focuses on building a student performance forecasting system supported by MLOps practices. The project leverages machine learning models to predict academic outcomes of students based on various performance indicators, while ensuring automation, reproducibility, and continuous deployment through CI/CD pipelines. The implementation combines data preprocessing, feature engineering, model training, and evaluation with cloud-based deployment frameworks such as AWS and Azure. By embedding monitoring, version control, and automated workflows, the system not only improves model accuracy but also enhances maintainability and scalability in real-world educational environments. The findings of this research demonstrate how MLOps can bridge the gap between machine learning development and practical deployment, ultimately helping educators and institutions make data-driven decisions to improve student success.},
        keywords = {},
        month = {September},
        }