The Carbon footprint of Machine Learning Models
Abstract
Machine Learning models are growing increasingly powerful in their abilities, whether that might be in processing natural language, tackling the intricacies of computer vision or any other number of exciting application that are emerging . But the environmental impact of machine learning models is increasingly receiving attentions. Here ,the works to focus on the carbon footprint of language models, as these models grow larger and larger, do their corresponding carbon footprints, especially when it comes to creating and training complex models. Here we will take a look at some concrete example of carbon emissions from machine learning models, will present tools that can be used to estimate the carbon footprint of a machine learning models. Finally present ideas for how to reduce the carbon footprint.
Keywords:
machine learning models, Carbon footprintsPublished
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Parvathy S Kurup, Pranav P Nair, Sai Kishor, Aryan S Nair, Pranav P, Face Image Synthesis , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Prayag Suresh, Sneha Susan Alex, Rojan Varghese, Thomas Zacharias, Shiney Thomas, Survey of Strabismus Detection Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Athul Das, Dan Kuruvilla, Amrutha P Chandran, Blesson V Monichan, Elias Janson K, TRIMBOT: AUTONOMOUS GRASS CUTTING ROBOT USING GPS NAVIGATION , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Honey Thomas, Linna Benny, Saya Nezrin, Navya Neethi S, Niya Joseph, Smart Communication Software for the Hearing Impaired Using Artificial Intelligence , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Aleena Joseph, Diya Paramesh G, Elza Mary Thomas, Gayathri V, Anu V Kottath, A Review on Comparison of VGG-16 and DenseNet algorithms for analysing brain tumor in MRI image , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
You may also start an advanced similarity search for this article.