Relevant bibliographies by topics / AI model deployment

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI model deployment'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "AI model deployment"

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Swamy, Prasadarao Velaga. "Continuous Deployment of AI Systems: Strategies for Seamless Updates and Rollbacks." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 6, no. 6 (2018): 1–8. https://doi.org/10.5281/zenodo.12805458.

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The deployment of artificial intelligence (AI) systems poses unique challenges compared to traditional software applications, primarily due to the dynamic nature of AI models and their sensitivity to data changes. Continuous deployment (CD) strategies play a crucial role in managing these complexities by enabling organizations to deploy, update, and manage AI models seamlessly and efficiently. This paper reviews key strategies for implementing CD in AI systems, focusing on seamless updates and robust rollback mechanisms. Strategies discussed include incremental deployment, A/B testing, canary
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Vijayan, Naveen Edapurath. "Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem37784.

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As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for implementing
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Sudheer Obbu. "Building a Robust CI/CD Pipeline for AI-Powered Cloud Applications." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 215–25. https://doi.org/10.32996/jcsts.2025.7.3.25.

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The deployment of AI applications in cloud environments presents unique challenges that traditional CI/CD pipelines fail to address, particularly in model versioning, data quality management, and system integration. This paper presents a comprehensive framework for building AI-specific CI/CD pipelines that effectively bridge these gaps. Through empirical analysis of successful implementations, we demonstrate how specialized pipeline architectures incorporating automated testing, intelligent resource allocation, and continuous monitoring can reduce deployment incidents by 37% while improving mo
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Sharma, Ankush. "Green AI: Minimizing Environmental Cost of AI Model Training and Deployment." ADHYAYAN: A JOURNAL OF MANAGEMENT SCIENCES 14, no. 02 (2024): 28–30. https://doi.org/10.21567/adhyayan.v14i2.06.

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The rapid development of artificial intelligence (AI), particularly deep learning models, has contributed to transformative innovations across various industries. The environmental influence of AI model training and deployment, especially energy consumption and carbon emissions through large-scale computational tasks, has gained increasing attention. This paper explores the concept of “Green AI,” a framework that emphasises minimizing the environmental costs of AI without sacrificing performance. By examining current practices in model development, energy consumption during training, and the r
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Sachin, Samrat Medavarapu. "Demystifying AI: A Comprehensive Review of Explainable AI Techniques and Applications." European Journal of Advances in Engineering and Technology 10, no. 6 (2023): 49–52. https://doi.org/10.5281/zenodo.13627267.

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Explainable Artificial Intelligence (XAI) seeks to make AI systems more transparent and understandable to users. This review examines the various techniques developed to achieve explainability in AI models and their applications across different domains. We discuss methods such as feature attribution, model simplification, and example-based explanations, highlighting their strengths and limitations. Additionally, we explore the importance of XAI in critical fields like healthcare, finance, and law. The findings underscore the necessity of explainability for trust, accountability, and ethical A
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Isabirye Edward Kezron. "Securing the AI supply chain: Mitigating vulnerabilities in AI model development and deployment." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2336–46. https://doi.org/10.30574/wjarr.2024.22.2.1394.

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The rapid advancement and integration of Artificial Intelligence (AI) across critical sectors — including healthcare, finance, defense, and infrastructure — have exposed an often-overlooked risk: vulnerabilities within the AI supply chain. This research examines the security challenges and potential threats affecting AI model development and deployment, focusing on adversarial attacks, data poisoning, model theft, and compromised third-party components. By dissecting the AI supply chain into its core stages — data sourcing, model training, deployment, and maintenance — this study identifies ke
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Toluwase Peter Gbenle, Abraham Ayodeji Abayomi, Abel Chukwuemeke Uzoka, Oyejide Timothy Odofin, Oluwasanmi Segun Adanigbo, and Jeffrey Chidera Ogeawuchi. "Developing an AI Model Registry and Lifecycle Management System for Cross-Functional Tech Teams." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 4 (2024): 442–56. https://doi.org/10.32628/ijsrset25121179.

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This paper presents a comprehensive solution for managing AI models across their lifecycle through the development of an AI model registry and lifecycle management system. As AI continues to play a crucial role across industries, the complexity of managing models—from development to deployment—presents significant challenges, especially within cross-functional teams. These challenges include issues such as model versioning, metadata management, deployment inconsistencies, and communication breakdowns among data scientists, engineers, and business stakeholders. The proposed system addresses the
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Prudhvi, Naayini, and Bura Chiranjeevi. "Optimizing AI Model Inference on Serverless Cloud Platforms: A Scalable Approach." International Journal of Current Science Research and Review 08, no. 05 (2025): 1927–35. https://doi.org/10.5281/zenodo.15323189.

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Abstract : The increasing prevalence of Artificial Intelligence (AI) and Machine Learning (ML) models across various industries has highlighted the critical need for efficient and scalable deployment strategies. Traditional deployment methods often struggle with adapting to fluctuating demands and maintaining cost-effectiveness. Serverless computing has emerged as a promising solution to address these challenges. This paper investigates the deployment of AI models within serverless architectures on Amazon Web Services (AWS), specifically focusing on AWS Lambda and Knative. The study analyzes t
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Researcher. "CLOUD-BASED AI/ML MODEL DEPLOYMENT: A COMPARATIVE ANALYSIS OF MANAGED AND SELF-MANAGED PLATFORMS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 1380–96. https://doi.org/10.5281/zenodo.14500931.

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The widespread adoption of artificial intelligence and machine learning (AI/ML) technologies has created an urgent need for efficient and scalable deployment solutions across industries. This article presents a comprehensive analysis of cloud-based AI/ML model deployment strategies, examining both managed platforms offered by major cloud providers (AWS SageMaker, Google Vertex AI, and Microsoft Azure Machine Learning) and self-managed infrastructure solutions. Through systematic evaluation of platform capabilities, infrastructure requirements, and organizational considerations, the article dev
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Gaurav Samdani, Kabita Paul, and Flavia Saldanha. "Serverless architectures for agentic AI deployment." World Journal of Advanced Engineering Technology and Sciences 7, no. 2 (2022): 320–33. https://doi.org/10.30574/wjaets.2022.7.2.0144.

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This paper presents directions on improving scalabilities, costs, and flexibility in serverless architectures incorporating agentic AI deployment. Using event-driven and a pay-as-you-go model, Serverless computing is shown to be an optimal way to deploy agentic AI systems due to their need for flexibility. The research objectives include the assessment of the possibilities for serverless platforms, the assessment of the effectiveness of its case applications, and the development of a solid methodology for its application in real life. The methodology uses case studies, comparative analysis, an
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Dissertations / Theses on the topic "AI model deployment"

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WoldeMichael, Helina Getachew. "Deployment of AI Model inside Docker on ARM-Cortex-based Single-Board Computer : Technologies, Capabilities, and Performance." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17267.

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IoT has become tremendously popular. It provides information access, processing and connectivity for a huge number of devices or sensors. IoT systems, however, often do not process the information locally, rather send the information to remote locations in the Cloud. As a result, it adds huge amount of data traffic to the network and additional delay to data processing. The later feature might have significant impact on applications that require fast response times, such as sophisticated artificial intelligence (AI) applications including Augmented reality, face recognition, and object detecti
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Books on the topic "AI model deployment"

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McKeon, Emily, Ross Smith Jr, and Mayte González. AI Revolution in Customer Service and Support: A Practical Guide to Impactful Deployment of AI Models. Pearson Education, 2024.

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Book chapters on the topic "AI model deployment"

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Munetsi, Dennis. "Prefiguring Afro-Centric and Inclusive AI Digital Commons: A Normative African Perspective to AI Development, Deployment, and Governance." In Trustworthy AI. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75674-0_2.

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Abstract This chapter critically explores the nexus of technology, society, and power, emphasising Africa’s integral role in global technological innovation and challenging the notion of technology as a neutral entity, instead framing it as a tool that can perpetuate existing power imbalances. It argues that technology serves as a political tool for dominant groups and is shaped by historical factors that have kept the continent under domination and oppression. Hence, the chapter proposes a prefigurative Afro-centric approach to the development of AI imbued with African epistemologies as a tool for expressing African politics and power in global socio-technological constellations—an argument for technology that can represent African interests on the world stage. The chapter contrasts incremental innovation with the need for radical change in Africa as a traditional technological periphery, underscoring the urgency for significant change within Africa’s technology sector. The development of African AI must navigate the complexities of evolving state governance, where traditional power is challenged by globalisation and regional integration, leading to hybrid governance arrangements of shared sovereignty. A three-tiered governance model is then proposed, advocating for scalable AI solutions that are inclusive and adaptable to the continent’s specificities, with a participatory approach that extends beyond elite circles to encompass a broader range of stakeholders. This model envisions a unified African AI strategy that leverages local, national, and regional diversities for collective advancement and equitable distribution of technological benefits.
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Soldatos, John, Babis Ipektsidis, Nikos Kefalakis, and Angela-Maria Despotopoulou. "Reference Architecture for AI-Based Industry 5.0 Applications." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46452-2_1.

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AbstractIndustry 5.0 (I5.0) is a novel paradigm for the development and deployment of industrial applications based on Cyber-Physical Systems (CPS). It evolves Industry 4.0 in directions that exploit trustworthy human–AI interactions in human-in-the-loop scenarios. Despite the rising popularity of I5.0, there is still a lack of reference architectures (RAs) that outline the building blocks of I5.0 applications, along with the structuring principles for effectively integrating them in industrial systems. This chapter introduces a reference model for industrial applications that addresses critical elements and requirements of the I5.0, including human–robot collaboration, cybersecurity, safety, and trust. The model enhances state-of-the-art I4.0 Industrial Internet of Things (IIoT) architectures with human-centered I5.0 features and functionalities. Based on this model, the present chapter introduces a set of blueprints that could ease the development, deployment, and operation of I5.0 applications. These blueprints address technical integration, trustworthy operations, as well as the ever-important compliance to applicable regulations such as General Data Protection Regulation (GDPR) and the emerging AI Act.
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Witt, Nicolas, Mark Deutel, Jakob Schubert, Christopher Sobel, and Philipp Woller. "Energy-Efficient AI on the Edge." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_19.

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AbstractThis chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal solutions that satisfy both resource restrictions (energy and memory) and AI performance. To accelerate the design of energyefficient classical machine learning pipelines, an AutoML tool based on evolutionary algorithms is presented, which uses an energy prediction model from assembly instructions (prediction accuracy 3.1%) to integrate the energy demand into a multiobjective optimization approach. For the deployment of deep neural network-based AI models, deep compression methods are exploited in an efficient design space exploration technique based on reinforcement learning. The resulting DNNs can be executed with a self-developed runtime for embedded devices (dnnruntime), which is benchmarked using the MLPerf Tiny benchmark. The developed methods shall enable the fast development of AI functions for the edge by providing AutoML-like solutions for classical as well as for deep learning. The developed workflows shall narrow the gap between data scientist and hardware engineers to realize working applications. By iteratively applying the presented methods during the development process, edge AI systems could be realized with minimized project risks.
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Ayuso, Mikel, Ander Muniategui, Aitor Aguirre-Ortuzar, and Enaitz Ezpeleta. "Laser Metal Deposition (LMD) Process Monitoring: From 3D Visualization of Sensor Data Towards Anomaly Detection." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_4.

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Abstract Metal Additive Manufacturing (AM) allows producing geometrically complex metal components, unlocking new design possibilities and making it suitable to sectors such as healthcare, automotive and aerospace. AM processes are complex and require the use of many sensors to extract relevant process information for its monitoring and control. In the last years, many studies have applied advanced Deep Learning methods to extract knowledge from AM processes. However, these developments are specific to a particular setup, problem or defectology. Furthermore, they lack frameworks and pipelines to guide throughout their development, and do not include AI-related tools for data labelling, visualization, and AI model development and deployment. With the aim of simplifying the development and deployment of AM process monitoring systems, a dashboard-based framework that makes use of AI for anomaly detection and for feature extraction is presented in this study. The framework helps with development and deployment of monitoring systems by easing the incorporation of new sensors and the extraction of new features from captured data by end users. In this study, a Laser Metal Deposition (LMD) process is considered as the use case to show the usefulness of the developed framework.
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Aliferis, Constantin, and Gyorgy Simon. "The Development Process and Lifecycle of Clinical Grade and Other Safety and Performance-Sensitive AI/ML Models." In Health Informatics. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39355-6_6.

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AbstractThis chapter introduces the notion of “clinical-grade” and other sensitive, mission-critical models and contrasts such models with more fault-tolerant feasibility, exploratory, or pre-clinical ones. The steps outlined span from requirements engineering to deployment and monitoring and also emphasize a number of contextual factors determining success such as clinical and health economic considerations. AI’s “knowledge cliff” is discussed and the need to operationalize AI/ML “self-awareness” and overcome its limitations to ensure generality and safe use. This chapter introduces many core pitfalls and best practices. The overarching concepts, pitfalls and BPs of the chapter will be elaborated further and implementation will be presented across the book and especially in chapters “Foundations and Properties of AI/ML Systems,” “An Appraisal and Operating Characteristics of Major ML Methods Applicable in Healthcare and Health Science,” “Foundations of Causal ML”, “Model Selection and Evaluation”, and in chapter “Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI”.
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Cai, Nishui, Zhuxiang Deng, and Hao Wang. "An Intelligent Data Flow Security Strategy Model of Cloud-Network Integration." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_1.

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AbstractCloud-network integration business data flow security is mainly reflected in the business deployment stage and online service stage. First, this paper analyzes the trend of the digital platform technology of the cloud-network integration business system, puts forward an intelligent data flow security strategy model of cloud-network integration, including expert rule judgment system of simple cloud scene and AI algorithm application model of complex cloud scene. Then, this paper studies hierarchical linkage cloud-network integration security operation system based on the security policy model of intelligent data flow and risk monitoring capability system for personal privacy data protection by scenario system based on the security policy model of intelligent data flow. Finally, this paper points out that cloud-network integration intelligent data flow security strategy based on AI algorithms needs to be further studied.
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Swathi, Y., and Manoj Challa. "From Deployment to Drift: A Comprehensive Approach to ML Model Monitoring with Evidently AI." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4657-6_22.

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Deuschel, Jessica, Andreas Foltyn, Karsten Roscher, and Stephan Scheele. "The Role of Uncertainty Quantification for Trustworthy AI." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_5.

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AbstractThe development of AI systems involves a series of steps, including data acquisition and preprocessing, model selection, training, evaluation, and deployment. However, each of these steps involves certain assumptions that introduce inherent uncertainty, which can result in inaccurate outcomes and reduced confidence in the system. To enhance confidence and comply with the EU AI Act, we recommend using Uncertainty Quantification methods to estimate the belief in the correctness of a model’s output. To make these methods more accessible, we provide insights into the possible sources of uncertainty and offer an overview of the different available methods. We categorize these methods based on when they are used in the process, accounting for various application requirements. We distinguish between three types: data-based, architecture-modifying and post-hoc methods, and share our personal experiences with each.
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Soldatos, John, Ernesto Troiano, Pavlos Kranas, and Alessandro Mamelli. "A Reference Architecture Model for Big Data Systems in the Finance Sector." In Big Data and Artificial Intelligence in Digital Finance. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94590-9_1.

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AbstractIn recent years there is a surge in the amount of digital data that are generated by financial organizations, which is driving the development and deployment of novel Big Data and Artificial Intelligence (AI) applications in the finance sector. Nevertheless, there is still no easy and standardized way for developing, deploying and operating data-intensive systems for digital finance. This chapter introduces a standards-based reference architecture model for architecting, implementing and deploying big data and AI systems in digital finance. The model introduces the main building blocks that comprise machine learning and data science pipelines for digital finance applications, while providing structuring principles for their integration in applications. Complementary viewpoints of the model are presented, including a logical view and considerations for developing and deploying applications compliant to the reference architecture. The chapter ends up presenting a few practical examples of the use of the reference model for developing data science pipelines for digital finance.
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Saeed, Muhammad, Hassaan Muhammad, Narmeen Sabah, et al. "Reinforcement Learning to Improve Finite Element Simulations for Shaft and Hub Connections." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_26.

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Abstract Advancements in technology and numerical methods have shifted from slow, resource-intensive software to faster predictive solutions powered by artificial intelligence (AI). An exemplary case is the analysis of interference fit connections between a cylindrical shaft and hub, which has the potential to redefine optimal design, minimizing stress and maximizing torque transmission. Traditional experimental analysis using Finite Element Method (FEM) simulations is undeniably time-consuming, inefficient, and complex, thus necessitating the deployment of AI as a pivotal tool in industrial applications. This paper unequivocally introduces a cutting-edge technique that harnesses two powerful AI approaches: Supervised Learning and Reinforcement Learning. The Reinforcement Learning approach expounded in this paper impeccably predicts the shaft-hub geometry set, eliminating the need for iterative simulations and drastically streamlining the optimization process. In order to address this challenge, a Supervised Learning model is rigorously trained using limited data obtained from experimental structural analysis. Subsequently, the predictions from this model serve as the environment for the Reinforcement Learning (RL) algorithm. The customized environment in Reinforcement Learning ingeniously employs the model to refine predictions by adjusting the input parameters for different geometric sets through respective actions on the environment.
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Conference papers on the topic "AI model deployment"

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Jhingran, Sushant, Nidhi Bansal, Rekha Chaturvedi, Ajeet Singh, and Yojna Arora. "Decentralized Generative AI Model Deployment Using Microservices." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882424.

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Liang, Yuxin, Peng Yang, Yuanyuan He, and Feng Lyu. "Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901571.

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Raj, Vijilius Helena, N. Sreevani, H. Pal Thethi, Neeraj Chahuan, Errabelli Annapoorna, and Sajjad Ziara. "Deployment of AI Model to Analyze the Automation Process of HR." In 2025 International Conference on Next Generation Communication & Information Processing (INCIP). IEEE, 2025. https://doi.org/10.1109/incip64058.2025.11020438.

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Conley, Jack, Nikhil Yadav, and Penelope Yanez. "Edge-X: Cost Factor Evaluation Workflow for Model Deployment on Edge AI Devices." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00123.

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Niu, Zifeng, Manuel Roveri, and Giuliano Casale. "ChainNet: A Customized Graph Neural Network Model for Loss-Aware Edge AI Service Deployment." In 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2024. http://dx.doi.org/10.1109/dsn58291.2024.00034.

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Fryda, Jan, Mykhailo Naumenko, Jan Nusko, and David Nikodem. "A Deployment Plan for an AI Model to Support Coastal Infrastructure Damage Assessment from a Tropical Storm." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642191.

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Rani, Fatima, Fenin Jose, Lucas Vogt, and Leonhard Urbas. "A Comparative Analysis of Industrial MLOps prototype for ML Application Deployment at the edge devices." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.152203.

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This paper introduces a prototype for constructing an edge AI system utilizing the contemporary Machine Learning Operations (MLOps) concept. By employing microcontrollers such as the Raspberry Pi as hardware, our methodology includes data scrubbing and machine learning model deployment on edge devices. Crucially, the MLOps pipeline is fully developed within the ecoKI platform, a research platform for ML/AI applications. In this study, we thoroughly investigate the performance of our ecoKI platform by comparing it with the established Edge Impulse platform. We deployed the ML model with differe
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Shekhar, Sudhanshu, Sathwik T. S, Mayank Pritwani, Mohana, Ramakanth Kumar P, and Sreelakshmi K. "Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI Based Computer Vision Applications." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914836.

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Salim, Soja, Jayasudha J. S., and Soniya B. "Safe and Trustworthy AI Framework (STAIF): Addressing Uncertainty in AI-Based Models for Real-World Deployment." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902880.

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Panchal, Deven, Prafulla Verma, Isilay Baran, Teyu Hsiung, Dan Musgrove, and David Lu. "Reusable MLOps: Reusable Deployment, Reusable Infrastructure and Hot-Swappable AI models and services." In 2024 10th International Conference on Smart Computing and Communication (ICSCC). IEEE, 2024. http://dx.doi.org/10.1109/icscc62041.2024.10690392.

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Reports on the topic "AI model deployment"

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Strauss, Ilan, Isobel Moure, Tim O’Reilly, and Sruly Rosenblat. The State of AI Governance Research: AI Safety and Reliability in Real World Commercial Deployment. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4112.d.2025.

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Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (Jan- uary 2020 - March 2025), we compare research outputs of leading AI companies (An- thropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that cor- porate AI research increasingly concentrates on pre-deployment areas — model align- ment and testing & evaluation — while attention to deployment-stage issues, such as model bias, has waned, as commercial imperatives and existential risks have come into focus. We fi
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Stankovic, Mirjana, and Nikola Neftenov. Cross Pollination and Digitalization of Public Sector Data: Opportunities and Challenges. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004355.

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This report aims to aid governments in Latin America and the Caribbean in embracing the opportunities public sector data utilization and artificial intelligence (AI) deployment can provide in achieving a circular economy model and the UNs Sustainable Development Goals (SDGs). For such purpose, the report provides a novel concept of sharing data between key players that we have named data cross-pollination. Drawing on this concept, it considers four SDGs, i.e., energy, sustainable food systems, reducing pollution, and smart cities. Building on case studies and initiatives, the report highlights
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Research Libraries Guiding Principles for Artificial Intelligence. Association of Research Libraries, 2024. http://dx.doi.org/10.29242/principles.ai2024.

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Artificial intelligence (AI) technologies, and in particular, generative AI, have significant potential to improve access to information and advance openness in research outputs. AI also has the potential to disrupt information landscapes and the communities that research libraries support and serve. The increasing availability of AI models sparks many possibilities and raises several ethical, professional, and legal considerations. Articulating a set of research library guiding principles for AI is useful to influence policy and advocate for the responsible development and deployment of AI te
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