Harnessing TensorFlow, PY Torch, and Scikit-Learn for Sustainable AI Projects
DOI:
https://doi.org/10.15680/IJCTECE.2023.0603001Keywords:
Sustainable AI, TensorFlow, PyTorch, Scikit-Learn, Model Compression, Green AI, Machine Learning, Optimization, Energy Efficiency, Carbon Footprint, Hardware AccelerationAbstract
In the rapidly evolving field of Artificial Intelligence (AI), creating sustainable AI solutions is a key challenge. As AI models grow increasingly complex, the environmental cost of training large-scale models becomes a critical consideration. This paper explores the integration of three widely-used machine learning frameworks— TensorFlow, PyTorch, and Scikit-Learn—in designing energy-efficient and sustainable AI projects. We evaluate strategies for optimizing model performance while minimizing carbon footprints, leveraging techniques like model compression, efficient architectures, and hardware acceleration. Case studies demonstrate the application of these frameworks in real-world scenarios, emphasizing energy-conscious model training and deployment. Through this, the paper provides a roadmap for AI practitioners aiming to design more sustainable machine learning pipelines.
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