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Found 12 papers, 3 papers with code
Identifying architectural design decisions for achieving green ML serving
The aim is to analyze ML serving architectural design decisions for the purpose of understanding and identifying them with respect to quality characteristics from the point of view of researchers and practitioners in the context of ML serving literature.
Lessons Learned from Mining the Hugging Face Repository
Our objective is to provide a practical guide for future researchers embarking on mining software repository studies within the HF ecosystem to enhance the quality of these studies.
Towards green AI-based software systems: an architecture-centric approach (GAISSA)
Nowadays, AI-based systems have achieved outstanding results and have outperformed humans in different domains.
Do DL models and training environments have an impact on energy consumption?
This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models.
Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
This study seeks to answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models?
Energy Efficiency of Training Neural Network Architectures: An Empirical Study
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures.
Teaching MLOps in Higher Education through Project-Based Learning
Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab.
Guiding the retraining of convolutional neural networks against adversarial inputs
Our goal is to improve the models against adversarial inputs regarding accuracy, resource utilization and time from the point of view of a data scientist in the context of image classification.
Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?
Overall, we plan to model the accuracy and complexity of AI-enabled applications in operation with respect to their design decisions and will provide tools for allowing practitioners to gain consciousness of the quantitative relationship between the design decisions and the green characteristics of study.
Software Engineering for AI-Based Systems: A Survey
Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Integration of Convolutional Neural Networks in Mobile Applications
In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity.
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment.
