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@article{178149,
        author = {Deekshith K A and Charan G and Y Amarnath Chowdhary and Vignesh R and K R Vishnu Kumar},
        title = {EXPERIMENT TRACKING AND OPTIMIZATION USING MLOPS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8330-8336},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178149},
        abstract = {We suggest the creation and deployment of an experiment tracking and optimization system based on MLOps principles. The system will utilize DVC (Data Version Control) to effectively manage and track a variety of machine learning experiments by changing hyperparameters, configurations, and workflows. In contrast to conventional model-centric methods, our emphasis is on the experimentation process itself, guaranteeing reproducibility, scalability, and automation in ML workflows. The system to be proposed will support effortless versioning of experiments, automatic tracking of results, and organized optimization of hyperparameters so that comparing different runs becomes less complicated and identifying the optimal configuration becomes more feasible. With Git for versioning and DVC for pipeline control, we set up a sound framework for dealing with iterative experimentation. This work describes the design, deployment, and assessment of our MLOps-driven experiment tracking system, emphasizing its advantages in enhancing workflow reproducibility, performance tracking, and parameter tuning. Furthermore, we discuss possible extensions including automatic hyperparameter tuning, richer visualization of experiment output, and incorporation with cloud-based ML systems. Our methodology is designed to simplify the ML development process, making the process of experimentation more efficient, transparent, and scalable.},
        keywords = {MLOPs, DVC, ML},
        month = {May},
        }