GreenMLX : an enhanced energy-efficient automated machine learning pipeline | |
| Author | Phuyal, Ashmita |
| Call Number | AIT Thesis no.DSAI-25-01 |
| Subject(s) | Machine learning Energy conservation |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | The increasing adoption and scale of automated machine learning (AutoML) systems have raised concerns regarding their environmental impact, particularly due to the sub stantial energy consumption and associated carbon emissions incurred during model training. While AutoML frameworks simplify model development, they often intensify resource utilization through extensive hyperparameter tuning and search processes. To address these challenges, this study proposes GreenMLX, a modular energy-efficient AutoML pipeline that integrates multi-objective optimization using Optuna, enabling simultaneous optimization of predictive accuracy, energy consumption, and training time. The pipeline also incorporates early stopping strategies, real-time emissions tracking via CodeCarbon, and support for model compression techniques aimed at reducing the environmental footprint. GreenMLX was evaluated against baseline AutoML frameworks—FLAML and AutoGluon—across ten benchmark tabular datasets from OpenML. Experimental results demonstrate that the Green pipelines achieved substantial reductions in energy consumption (up to 99.88%) and carbon emissions (up to 99.94%), with minimal degradation in model accuracy (within 3% of baseline performance in most cases). A detailed case study on the Household Power Consumption dataset further confirmed that Green pipelines effectively reduce energy usage, carbon emissions, and training time without compromising predictive performance.Moreover, explainability analyses using SHAP (SHapley Additive exPlanations) provided in sights into model behavior, validating that GreenMLX models maintained interpretabil ity while achieving sustainability objectives. These findings highlight the feasibility of integrating energy-efficient practices into AutoML workflows, offering a practical path toward environmentally sustainable machine learning without sacrificing model effectiveness or transparency. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Chutiporn Anutariya |
| Examination Committee(s) | Virdis, Salvatore G.P.;Chantri Polprasert |
| Scholarship Donor(s) | AIT Scholarship |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2025 |