[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118497-en":3,"doc-seo-118497-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118497,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Lightweight Machine Learning Models with Python for Green AI - Paper","Growing demand for machine learning across industries intensifies concern over environmental impact from training large models. Green AI addresses this by developing energy-efficient models that use fewer computational resources while preserving performance. This paper investigates how lightweight machine learning models, implemented with Python, support Green AI practices. It reviews compact-design methods including model pruning, knowledge distillation, and efficient architectures such as decision trees, linear models, and lightweight neural networks. Using practical Python examples, it shows how these techniques can lower the carbon footprint of AI systems without sacrificing accuracy.","International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM)  \n| ISSN: [2395-7639 |](2395-7639 | www.ijmrsetm.com | Impact Factor:)[ ](2395-7639 | www.ijmrsetm.com | Impact Factor:)[www.ijmrsetm.com](2395-7639 | www.ijmrsetm.com | Impact Factor:)[ | Impact Factor:](2395-7639 | www.ijmrsetm.com | Impact Factor:) 7.802 | A Monthly Double-Blind Peer Reviewed Journal |  \n| Volume 11, Issue 6, June 2024 |  \nLightweight Machine Learning Models with  \nPython for Green AI  \nIshita Manoj Verma  \nDepartment of Computer Science & Engineering, Parul University, Vadodara, Gujarat, India  \nABSTRACT: With the increasing demand for machine learning (ML) applications across various industries, the environmental impact of training large models has become a significant concern. Green AI emphasizes the development of machine learning models that are energy-efficient, requiring fewer computational resources while maintaining high performance. This paper explores how lightweight machine learning models, implemented with Python, can contribute to Green AI practices. We review several approaches for designing compact models, including model pruning, knowledge distillation, and efficient architectures such as decision trees, linear models, and lightweight neural networks. By adopting these techniques, organizations can reduce the carbon footprint of AI systems without compromising accuracy. Through practical examples, we demonstrate how Python libraries and tools can facilitate the creation of lightweight models in an energy-efficient manner.  \nKEYWORDS:  \n• Green AI  \n• Lightweight Models  \n• Energy-Efficient Machine Learning  \n• Python for AI  \n• Model Pruning  \n• Knowledge Distillation  \n• Efficient Neural Networks  \n• Sustainability in AI  \n• Energy Consumption Optimization  \nI. INTRODUCTION  \nMachine learning (ML) has made tremendous advancements over the last decade, leading to significant breakthroughsin natural language processing, computer vision, and other fields. However, the rapid increase in the size and complexity of machine learning models has raised concerns about the environmental impact of training these models. Large models, especially deep learning architectures, consume substantial computational resources, contributing to high energy consumption and a large carbon footprint.  \nIn response to these concerns, the concept of Green AI has emerged. Green AI focuses on developing sustainable, energy-efficient models without sacrificing performance. One key strategy for achieving this goal is the use of lightweight machine learning models, which are simpler, smaller in size, and less computationally demanding. This paper aims to explore how lightweight models can be effectively built using Python and popular libraries, contributing to the Green AI movement.  \nII. LITERATURE REVIEW  \nThe need for energy-efficient AI models has been widely recognized in recent research. Studies have shown that largescale models, such as deep neural networks, require enormous computational power and energy to train, often leading to significant environmental impact (Strubell et al., 2019) . As a result, various approaches have been proposed to address this issue.  \n• Model Pruning: Pruning involves removing unnecessary parameters from a model, making it lighter and faster while retaining its performance. Research by Han et al. (2015) demonstrated that pruning deep neural networks could reduce model size and computational complexity without significantly impacting accuracy.  \nIJMRSETM©2024 | An ISO 9001:2008 Certified Journal | 9373  \nInternational Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM)  \n| ISSN: [2395-7639 |](2395-7639 | www.ijmrsetm.com | Impact Factor:)[ ](2395-7639 | www.ijmrsetm.com | Impact Factor:)[www.ijmrsetm.com](2395-7639 | www.ijmrsetm.com | Impact Factor:)[ | Impact Factor:](2395-7639 | www.ijmrsetm.com | Impact Factor:) 7.802 | A Monthl","cbCaisVJ64Apo9Su","https://ap.wps.com/l/cbCaisVJ64Apo9Su","pdf",301753,1,6,"English","en",105,"# Abstract\n# Introduction\n## Green AI and lightweight models\n# Literature Review\n## Model pruning\n## Knowledge distillation\n## Efficient neural network architectures\n## Decision trees and linear models\n# Lightweight machine learning models and techniques","[{\"question\":\"What is Green AI, and why is it important for machine learning?\",\"answer\":\"Green AI focuses on building sustainable, energy-efficient models that maintain performance. It helps reduce the high energy use and carbon footprint associated with training large models.\"},{\"question\":\"How do model pruning techniques support energy-efficient machine learning?\",\"answer\":\"Model pruning removes unnecessary parameters or weights after training, making models lighter and faster. Research cited shows this can reduce size and computational complexity while largely retaining accuracy.\"},{\"question\":\"What role does knowledge distillation play in creating lightweight models?\",\"answer\":\"Knowledge distillation trains a smaller student model to mimic a larger teacher model. This can reduce deployment computational requirements while maintaining performance.\"}]","Lightweight Machine Learning Models with Python for Green AI - Paper | PDF",1785683875,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"lightweight-machine-learning-models-with-python-for-green-ai-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/lightweight-machine-learning-models-with-python-for-green-ai-paper/118497/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Green AI, and why is it important for machine learning?","Question",{"text":75,"@type":76},"Green AI focuses on building sustainable, energy-efficient models that maintain performance. It helps reduce the high energy use and carbon footprint associated with training large models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do model pruning techniques support energy-efficient machine learning?",{"text":80,"@type":76},"Model pruning removes unnecessary parameters or weights after training, making models lighter and faster. Research cited shows this can reduce size and computational complexity while largely retaining accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does knowledge distillation play in creating lightweight models?",{"text":84,"@type":76},"Knowledge distillation trains a smaller student model to mimic a larger teacher model. 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