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Journal articles on the topic 'AI model deployment'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Abdulrahman, Sahar, and Markus Trengove. "Mitigated deployment strategy for ethical AI in clinical settings." BMJ Health & Care Informatics 32, no. 1 (2025): e101363. https://doi.org/10.1136/bmjhci-2024-101363.

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Clinical diagnostic tools can disadvantage subgroups due to poor model generalisability, which can be caused by unrepresentative training data. Practical deployment solutions to mitigate harm for subgroups from models with differential performance have yet to be established. This paper will build on existing work that considers a selective deployment approach where poorly performing subgroups are excluded from deployments. Alternatively, the proposed ‘mitigated deployment’ strategy requires safety nets to be built into clinical workflows to safeguard under-represented groups in a universal dep

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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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Banerjee, Joyanta. "Scalable AI Model Deployment with AWS SageMaker and EKS." International Journal of Computer Trends and Technology 72, no. 11 (2024): 135–42. https://doi.org/10.14445/22312803/ijctt-v72i11p114.

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Ganesh, Prakhar. "Model Multiplicity for Responsible AI." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7, no. 2 (2025): 14–17. https://doi.org/10.1609/aies.v7i2.31896.

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Machine learning has experienced a remarkable rise, with highly sophisticated over-parameterized models leading the way. Consequently, these cutting-edge models find application across diverse domains. Their increasing deployment has sparked concerns about their real-world impact, studied under the umbrella of responsible AI. A crucial aspect of building responsible AI models is the idea of model multiplicity. If managed well, model multiplicity gives us the freedom to prioritize several metrics, including those associated with responsible AI, and select the best models to minimize harm. Howev

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R, Antony Roshan, Barath V, Deva Dharshini D, Dhilak M, and Saraswathi R. "LLM ENHANCED AI CHATBOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem40033.

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The development and deployment of a Large Language Model (LLM)-based tool designed to generate human-like responses to natural language inputs in a network isolated environment presents unique technical and logistical challenges. Such a tool leverages state-of-the-art Natural Language Processing (NLP) and machine learning techniques to simulate real-time, coherent, and contextually appropriate interactions without relying on an active internet connection. This approach involves training or fine-tuning a pre-existing LLM on domain-specific datasets and configuring the model to operate efficient

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Nikhila Pothukuchi. "Hardware-aware neural network training: A comprehensive framework for Efficient AI model deployment." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1831–38. https://doi.org/10.30574/wjaets.2025.15.1.0344.

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This article presents a comprehensive guide to hardware-aware training techniques for artificial intelligence models, addressing the critical balance between performance optimization and resource efficiency. The discussion encompasses key strategies including quantization methods for precision reduction, systematic network pruning for architecture refinement, sparsity implementation for model optimization, and hardware-specific adaptations. Through detailed exploration of these techniques, the article demonstrates how integrating hardware considerations during the training process leads to sub

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Prabu, Arjunan. "AI Model Management with AWS Cloud Infrastructure." International Journal on Science and Technology 15, no. 4 (2024): 1–5. https://doi.org/10.5281/zenodo.14514123.

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State-of-the-art AI development requires a strong infrastructure that could handle everything: from initial experimentation to the production deployment of a model. AWS offers an end-to-end suite of services that allows enterprises to build, train, deploy, and manage machine learning models at scale. This whitepaper details an enterprise approach to managing AI models using AWS Cloud Infrastructure with version control, reproducibility, and operational efficiency in mind.

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Savita Nuguri, Rahul Saoji, Krishnateja Shiva, Pradeep Etikani, and Vijaya Venkata Sri Rama Bhaskar. "OPTIMIZING AI MODEL DEPLOYMENT IN CLOUD ENVIRONMENTS: CHALLENGES AND SOLUTIONS." International Journal for Research Publication and Seminar 12, no. 2 (2021): 159–68. http://dx.doi.org/10.36676/jrps.v12.i2.1461.

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Among the studies related to the use of artificial intelligence in cloud compting, this research seeks to identify techniues that may help in the effectve implementation of models in cloud based sysems. Some of the main questions that are answered include cost control, working with multiple cloud services, achieving higher speed, preserving the privacy of inforation, and creating conitions for its safe storage, also provider migration. Possible solution instances include autoscaling, model compression, secure enclaves, and contaner for measurability tasks with a range of solutions being consde

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Naayini, Prudhvi. "Scalable AI Model Deployment and Management on Serverless Cloud Architecture." International Journal of Electrical, Electronics and Computers 9, no. 1 (2024): 1–12. https://doi.org/10.22161/eec.91.1.

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Scalable deployment of deep learning models in the cloud faces challenges in balancing performance, cost, and manageability. This paper investigates serverless cloud architecture for AI model inference, focusing on AWS technologies such as AWS Lambda, API Gateway, and Kubernetes-based serverless extensions (e.g., AWS EKS with Knative). We first outline the limitations of traditional, server-based model hosting to motivate the serverless approach. Then, we present novel strategies for scalable model serving: an adaptive resource provisioning algorithm, intelligent model caching, and efficient m

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Maddala, Suresh Kumar. "Understanding Explainability in Enterprise AI Models." International Journal of Management Technology 12, no. 1 (2025): 58–68. https://doi.org/10.37745/ijmt.2013/vol12n25868.

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This article examines the critical role of explainability in enterprise AI deployments, where algorithmic transparency has emerged as both a regulatory necessity and a business imperative. As organizations increasingly rely on sophisticated machine learning models for consequential decisions, the "black box" problem threatens stakeholder trust, regulatory compliance, and effective model governance. We explore the multifaceted business case for explainable AI across regulated industries, analyze the spectrum of interpretability techniques—from inherently transparent models to post-hoc explanati

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Mistry, Het. "Mastering Model Selection for AI/ML Models." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 55–67. https://doi.org/10.37745/ejcsit.2013/vol13n145567.

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This article presents a comprehensive framework for mastering model selection in artificial intelligence and machine learning applications across diverse domains. The article addresses the fundamental challenge of selecting models that optimally balance complexity with generalization capability, navigating the classic bias-variance tradeoff that underpins predictive performance. Beginning with theoretical foundations of regularization approaches and complexity measures, the article proceeds through data-driven selection strategies, including cross-validation techniques and advanced hyperparame

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Satyam, Chauhan. "Intelligent Edge Computing for IoT Data Processing and AI Model Deployment." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 4 (2023): 1–16. https://doi.org/10.5281/zenodo.14613685.

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The exponential growth of IoT devices and the rising demand for AI-driven applications have introduced significant challenges in data processing, scalability, and latency. Intelligent Edge Computing (IEC) emerges as a transformative solution by processing data closer to its source, thus addressing these challenges while enhancing privacy and reducing bandwidth usage. This paper explores the architecture, techniques, and strategies of IEC for IoT data processing and AI model deployment. Key topics include edge architecture, lightweight AI algorithms, federated learning, and transfer learning fo

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Biswas, Jeet. "NICKONN – AN AI-POWERED SEARCH ENGINE POWERED BY LLAMA MODEL." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03638.

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Abstract: With the growing volume of information on the internet, retrieving accurate, relevant, and context-aware content has become a significant challenge. Traditional search engines rely heavily on keyword matching and static ranking, often overlooking the semantic context behind user queries. This project presents Nickonn – An AI-Powered Search Engine, a modular, AI-enhanced, and privacy-first platform that delivers summarized, intelligent, and referenced responses by integrating open-source metasearch technology (SearxNG) with transformer-based language models such as GPT, LLaMA, and Mix

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Dr., Juby Mathew, Sen Easow Neil, Shankar Rajalakshmi, Babu Nandhu, and Pratap Singh Rudra. "Career Finder: AI powered career guider." International Journal on Emerging Research Areas (IJERA) 05, no. 01 (2025): 174–77. https://doi.org/10.5281/zenodo.15187120.

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This paper discusses the overall design, development, and deployment of an AI-based career recommendation system, organized into four interdependent modules: User Interface (UI) Design and Development, Backend Development and API Management, AI Model Integration and Recommendation Engine, and Database and Deployment. The platform leverages cutting-edge technologies such as React.js for a dynamic front-end, Flask for robust backend API development, OpenAI GPT-based models (or alternatives like Hugging Face Transformers or LLaMA) for personalized career insights, and MongoDB for scalable da

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Nakayama, Luis Filipe, João Matos, Justin Quion, et al. "Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review." PLOS Digital Health 3, no. 10 (2024): e0000618. http://dx.doi.org/10.1371/journal.pdig.0000618.

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Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps—data collection; defining the mo

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van der Vorst, Joris P., Jim M. Smit, Davy van de Sande, et al. "Importance of model governance in clinical AI models: case study on the relevance of data drift detection." BMJ Digital Health & AI 1, no. 1 (2025): e000046. https://doi.org/10.1136/bmjdhai-2025-000046.

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Objective To evaluate the use of data drift detection into an artificial intelligence (AI) governance framework to ensure the safe clinical deployment of an AI model that predicts safe patient discharge after gastrointestinal and oncological surgery. Despite the potential of AI in healthcare, clinical implementation remains limited. Mature AI governance is critical for safe and effective deployment, particularly in dynamic healthcare settings, where patient populations and treatment protocols evolve over time and data drift can occur. This case study illustrates the value of proactive model an

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Balaji, Soundararajan. "Engineering Systems for Dynamic Retraining and Deployment of AI Models." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 2 (2023): 1–9. https://doi.org/10.5281/zenodo.15054625.

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The increasing reliance on artificial intelligence (AI) in dynamic business environments enables the adaptive model management systems to mitigate performance degradation caused by evolving data patterns, operational shifts, and market changes. Traditional retraining methods are resource-intensive and struggle to maintain consistency, prompting the need for innovative approaches such as Just-in-Time (JIT) retraining, real-time monitoring, and automated deployment pipelines. We will examine the engineering challenges of designing adaptive AI systems, including scalability, computational costs,

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D, Aishwaya. "AI Driven Phishing Detection Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1023–25. https://doi.org/10.22214/ijraset.2025.70029.

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Phishing attacks are a significant cybersecurity threat as they trick people into revealing personal information through fake websites. This project introduces an integrated CNN-LSTM model to detect phishing URLs. It uses Convolutional Neural Networks (CNNs) to look for local patterns and Long Short-Term Memory (LSTM) networks to analyze the order of information in URLs. To further clarify, SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) are implemented, giving insights into how the model predicts. The trained model is served as a FastAPI/Flask w

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Kshirsagar, Meghana, Krishn Kumar Gupt, Gauri Vaidya, Conor Ryan, Joseph P. Sullivan, and Vivek Kshirsagar. "Insights Into Incorporating Trustworthiness and Ethics in AI Systems With Explainable AI." International Journal of Natural Computing Research 11, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijncr.310006.

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Over the past seven decades since the advent of artificial intelligence (AI) technology, researchers have demonstrated and deployed systems incorporating AI in various domains. The absence of model explainability in critical systems such as medical AI and credit risk assessment among others has led to neglect of key ethical and professional principles which can cause considerable harm. With explainability methods, developers can check their models beyond mere performance and identify errors. This leads to increased efficiency in time and reduces development costs. The article summarizes that s

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Harshavardhan1, Polamarasetty. "Design and Implementation of a Fine-Tuned Llama-Based AI Chatbot with Voice and Text Interaction Using Streamlit and Ollama." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42961.

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Natural language processing (NLP) drives artificial intelligence (AI)driven chatbots that have gained great popularity in many industries in recent years [15], therefore improving humancomputer interactions. Optimized for quick conversational reactions, this paper describes an AIdriven chatbot driven by a finely tuned Llama 3.2 model. Using Streamlit for an interactive user interface, Ollama for model deployment, and SpeechRecognition and pyttsx3 for smooth voice input and texttospeech (TTS) output [5][10[11]], the chatbot combines voice and textbased communication [2][4]. Using Sloth, a dedic

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Krones, Felix, and Benjamin Walker. "From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings." PLOS Digital Health 3, no. 12 (2024): e0000437. https://doi.org/10.1371/journal.pdig.0000437.

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This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: (1) The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Reasons for the limited deployment of machine learning models are discussed, as well as model generalisation. Moreover, the literature review discusses ho

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Fauveau, Valentin, Sean Sun, Zelong Liu, et al. "Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub." Bioengineering 10, no. 12 (2023): 1396. http://dx.doi.org/10.3390/bioengineering10121396.

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The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical ta

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Niglia, Francesco. "A Model for Responsible Governance of human-centric AI in the Public Sector." Community Notebook. People, Education and Welfare in the Society 5.0, no. 1 (April 30, 2025): 277–309. https://doi.org/10.61007/qdc.2025.1.288.

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Responsible AI Governance in the Public Sector is no longer an option due to the numerous ethical issues that have emerged in recent years with the adoption of AI-based services in the Public Sector. Given the numerous challenges AI poses, it is essential to incorporate human- centric and social perspectives. This study discusses a framework model for defining the roles, responsibilities, and skills of all the stakeholders involved in the processes of AI development, deployment, and assessment.

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Sherman, Eli, and Ian Eisenberg. "AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23047–52. http://dx.doi.org/10.1609/aaai.v38i21.30348.

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As AI systems’ sophistication and proliferation have increased, awareness of the risks has grown proportionally. The AI industry is increasingly emphasizing the need for transparency, with proposals ranging from standardizing use of technical disclosures, like model cards, to regulatory licensing regimes. Since the AI value chain is complicated, with actors bringing varied expertise, perspectives, and values, it is crucial that consumers of transparency disclosures be able to understand the risks of the AI system in question. In this paper we propose a risk profiling standard which can guide d

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Soundararajan, Balaji. "Developing New AI Model Compression Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–8. https://doi.org/10.55041/ijsrem43474.

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The rapid growth of artificial intelligence (AI) model complexity has created significant challenges for deployment on resource-constrained devices and customization by developers. Model compression techniques, such as pruning, quantization, and knowledge distillation, have emerged as critical solutions to reduce computational and memory demands while preserving accuracy. This work explores foundational and state-of-the-art approaches to AI model compression, emphasizing their role in enabling efficient edge computing, lowering energy consumption, and democratizing access to advanced AI capabi

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Kiran, Kumar Voruganti. "Edge-AI and IoT DevOps: Managing Deployment Pipelines for Real-Time Analytics." Journal of Scientific and Engineering Research 9, no. 6 (2022): 84–94. https://doi.org/10.5281/zenodo.12666911.

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The integration of Edge-AI and IoT within DevOps practices is revolutionizing data processing and real-time analytics, enabling immediate insights and decision-making across various industries. This paper explores the deployment of Edge-AI and IoT in DevOps environments, focusing on system architecture, automation, AI model training, real-time data processing, and security mechanisms. By examining the roles of edge computing nodes, AI model deployment, and real-time analytics, the study highlights the benefits of reduced latency, enhanced data privacy, and efficient resource utilization. Throu

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Bhaskar Goyal. "Understanding cloud-native AI: The foundation of scalable platform architecture." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 822–27. https://doi.org/10.30574/wjaets.2025.15.1.0251.

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Cloud-native AI represents a transformative paradigm shift in enterprise artificial intelligence deployment, fundamentally reimagining how organizations architect, deploy, and manage AI systems. By embracing containerization, microservices architecture, and declarative configuration, this approach enables unprecedented levels of scalability, resilience, and operational efficiency. The integration of Kubernetes orchestration with specialized hardware management creates a foundation for dynamically scaling AI workloads while optimizing resource utilization. Organizations implementing these archi

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Sharipov, Rinat. "Analysis and Reduction of Errors in AI Models." American Journal of Engineering and Technology 07, no. 05 (2025): 202–10. https://doi.org/10.37547/tajet/volume07issue05-20.

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The issue of errors in artificial intelligence (AI) models is a critical aspect that requires systematic analysis and the application of effective methods for their reduction. Errors in AI models can occur at various stages of development and deployment, including data collection, model training, and operation phases. The key tasks in this field involve identifying error sources and applying approaches aimed at eliminating them. Methods such as cross-validation, regularization, and the use of ensemble models play a significant role in reducing errors and improving prediction accuracy. Therefor

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Nihar, Malali. "THE ROLE OF DEVSECOPS IN FINANCIAL AI MODELS: INTEGRATING SECURITY AT EVERY STAGE OF AI/ML MODEL DEVELOPMENT IN BANKING AND INSURANCE." International Journal of Engineering Technology Research & Management (IJETRM) 06, no. 11 (2022): 218–25. https://doi.org/10.5281/zenodo.15239176.

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Artificial intelligence (AI) and machine learning (ML) technologies have brought revolutionary changes tofinancial institutions such as banks and insurers during their operations. The financial industry relies heavily onAI models for both automated underwriting policies and personalized recommendation services and fraudulentactivity discovery along with credit scoring assessments. The deeper financial institutions incorporate thesemodels into their systems them more vulnerability to cyberattacks. DevSecOps represents a revolutionary methodwhich includes security measures during every stage of

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Venkata Krishna Koganti. "Autonomous CI/CD Meshes: Self-healing deployment architectures with AI-ML Orchestration." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2731–45. https://doi.org/10.30574/wjaets.2025.15.2.0777.

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This article introduces a novel architecture for autonomous continuous integration and continuous deployment (CI/CD) systems capable of self-healing and self-optimization without human intervention. The article presents intelligent deployment meshes that integrate deep anomaly detection using LSTM networks with Bayesian change-point detection to identify deployment anomalies before they impact production environments. The proposed framework leverages causal CI/CD graphs to model complex interdependencies between microservices, enabling context-aware remediation strategies including automated r

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Tarafdar, Rajarshi. "SELF-HEALING AI MODEL INFRASTRUCTURE: AN AUTOMATED APPROACH TO MODEL DEPLOYMENT MAINTENANCE AND RELIABILITY." INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS 16, no. 1 (2025): 992–1004. https://doi.org/10.34218/ijitmis_16_01_071.

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Gupta, Shreya. "The Rise of Serverless AI: Transforming Machine Learning Deployment." European Journal of Computer Science and Information Technology 13, no. 5 (2025): 45–67. https://doi.org/10.37745/ejcsit.2013/vol13n54567.

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Serverless computing has revolutionized artificial intelligence deployment by introducing a paradigm shift in infrastructure management and resource utilization. The technology enables organizations to deploy AI solutions without managing underlying infrastructure, offering automatic scaling and pay-per-use pricing models. Function-as-a-Service dominates the market share, particularly in the Banking, Financial Services and Insurance sector, while Backend-as-a-Service gains traction in AI applications. Organizations achieve significant reductions in total cost of ownership while maintaining hig

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Li, Xinnuo. "Analysis of the Designs and Applications of AI Chip." Highlights in Science, Engineering and Technology 76 (December 31, 2023): 168–80. http://dx.doi.org/10.54097/k1p7yk27.

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The rapid evolution of deep learning model architectures and the increasing scale of model parameters have imposed heightened demands on deep learning training, inference, and deployment, leading to the swift advancement and unprecedented prosperity of AI chips. Therefore, this study sets out to analyze the designs and applications of AI chips by considering their unique requirements compared to conventional chips, and by combining software and hardware aspects. The paper delineates the classification of common AI chips along with their distinct design strategies and optimization algorithms. I

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Bhanu Prakash Kolli. "The rise of AI-Augmented DevOps: How human engineers and AI Co-manage cloud infrastructure." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1577–88. https://doi.org/10.30574/wjaets.2025.15.1.0270.

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The integration of artificial intelligence into DevOps practices represents a paradigm shift in cloud infrastructure management. As cloud environments grow increasingly complex with microservices architectures and multi-cloud deployments, traditional operational approaches are proving insufficient. Rather than replacing human engineers, AI-augmented DevOps serves as a collaborative force that enhances decision-making capabilities, automates routine tasks, and provides insights that are impossible to derive manually. This article explores several key dimensions of this emerging paradigm: AI-pow

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Khoroshylov, S. V., and V. K. Shamakhanov. "Deployment control of transformable rod structures using reinforcement learning." Technical mechanics 2025, no. 1 (2025): 63–76. https://doi.org/10.15407/itm2025.01.063.

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The task of controlling the deployment of transformable rod structures for space applications is studied. An example of such structures is a mesh antenna truss, which is deployed using a cable-pulley system. The aim of the study is to develop an intelligent agent (IA) based on the reinforcement learning (RL) methodology, which ensures the deployment and maintenance of the structure under consideration in the deployed position, taking into account the specified requirements. The main requirements are the deployment time and the minimum angular velocities of the V-folding rods at the final stage

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Lahlali, Mustapha, Naoual Berbiche, and Jamila El Alami. "Artificial Intelligence Operating Model: A Proposal Framework for AI Operationalization and Deployment." Journal of Computer Science 18, no. 11 (2022): 1100–1109. http://dx.doi.org/10.3844/jcssp.2022.1100.1109.

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Xu, Kan, Zhe Chen, Fu Xiao, Jing Zhang, Hanbei Zhang, and Tianyou Ma. "Semantic model-based large-scale deployment of AI-driven building management applications." Automation in Construction 165 (September 2024): 105579. http://dx.doi.org/10.1016/j.autcon.2024.105579.

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Malik, Rohan. "DevOps and MLOps: Integrating CI/CD Pipelines for Scalable AI Model Deployment." International Journal of Emerging Trends in Computer Science and Information Technology 3, no. 1 (2022): 1–7. https://doi.org/10.63282/3050-9246.ijetcsit-v3i4p101.

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Park, Jeman, Misun Yu, Jinse Kwon, Junmo Park, Jemin Lee, and Yongin Kwon. "NEST‐C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators." ETRI Journal 46, no. 5 (2024): 851–64. http://dx.doi.org/10.4218/etrij.2024-0139.

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AbstractDeep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general‐purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing‐in‐memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST‐C), a novel DL framework that improves the deployment and performance of models across vario

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Thota, Ravi Chandra. "AI-driven infrastructure automation: Enhancing cloud efficiency with MLOps and DevOps." INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT 6, no. 9 (2021): 1–11. https://doi.org/10.5281/zenodo.15041132.

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Cloud infrastructure management reached a reliable system structure that combines operational efficiency andscalability because of AI-driven automation. The research outlines strategies to integrate AI systems into DevOps andMLOps operations because they enhance cloud management performance outcomes. AI divides the automation solutioninto predictive analytics capabilities combined with workload adaptation features which also includes an automaticrecovery system designed to operate cloud infrastructure management. AI deployment within DevOps operations resultsin deployment acceleration of 40-60

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Chaudhuri, Ranjan, Sheshadri Chatterjee, Demetris Vrontis, and Sumana Chaudhuri. "Innovation in SMEs, AI Dynamism, and Sustainability: The Current Situation and Way Forward." Sustainability 14, no. 19 (2022): 12760. http://dx.doi.org/10.3390/su141912760.

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The purpose of this study is to examine artificial intelligence (AI) dynamism and its impact on sustainability of firms, including small and medium enterprises (SMEs). In addition, this study investigates the moderating effects of technological and leadership support for AI technology deployment and sustainability for manufacturing and production firms. We developed a theoretical model through the lenses of expectation disconfirmation theory (EDT), technology–trust–fit (TTF) theory, contingency theory, and the knowledge contained in the existing literature. We tested the proposed theoretical m

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