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@article{187266,
        author = {Ms. Snehal Ashok Mahajan and Ms. Shruti Pant and Mr. Anand Arvind Maha and Mr. Kunal Pandey},
        title = {Optimizing Hyperparameters at Scale: Large-Scale Automated Tuning for ML Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5675-5678},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187266},
        abstract = {Hyperparameter optimization (HPO) plays a crucial role in improving the performance, robustness, and generalization of machine learning systems. However, modern large-scale ML applications such as deep learning, recommendation engines, and distributed training require tuning thousands of hyperparameters across massive search spaces, making conventional HPO techniques inefficient. This research investigates scalable hyperparameter optimization frameworks using distributed computing, Bayesian optimization, multi-fidelity methods, evolutionary algorithms, and gradient-based approaches. We propose a hybrid large-scale HPO framework that integrates asynchronous parallel search, low-fidelity approximations, and adaptive early stopping. Experiments on benchmark datasets (ImageNet, CIFAR-100, and large NLP workloads) demonstrate up to 47% reduction in compute cost and 18–32% performance improvement over baseline tuning strategies. The proposed system provides a practical, scalable solution for real-world ML pipelines deployed in cloud and edge environments.},
        keywords = {Hyperparameter Optimization, Large-Scale ML Systems, Automated Machine Learning, Bayesian Optimization, Distributed Training, Multi-Fidelity Search, AutoML.},
        month = {November},
        }