Bibliographies thématiques / Deployment models

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Littérature scientifique sur le sujet « Deployment models »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 25 juillet 2025

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Articles de revues sur le sujet "Deployment models"

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Ravi, Shankar Koppula. "Databricks MLflow." Journal of Scientific and Engineering Research 8, no. 11 (2021): 134–45. https://doi.org/10.5281/zenodo.11232369.

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This paper examines MLflow, an open-source platform specifically designed to simplify the management of the machine learning lifecycle. It covers various aspects, such as experiment tracking, code packaging, and sharing and deployment of models. The paper focuses on the integration of MLflow with Databricks, emphasizing how this collaboration enhances automatic experiment tracking and provides easier access to data and models. This integration ultimately leads to more efficient and reproducible machine learning workflows. The paper thoroughly explores the four main components of MLflow: MLflow
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Reddy, Vonteru Srikanth, and Kumar Debasis. "Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 465–81. http://dx.doi.org/10.17762/ijritcc.v11i7s.7025.

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Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which incr
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Howick, R. S., and M. Pidd. "Sales force deployment models." European Journal of Operational Research 48, no. 3 (1990): 295–310. http://dx.doi.org/10.1016/0377-2217(90)90413-6.

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B. Patel, Prof Hiral, and Prof Nirali Kansara. "Cloud Computing Deployment Models: A Comparative Study." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (2021): 45–50. http://dx.doi.org/10.21276/ijircst.2021.9.2.8.

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Rimbaud, Loup, Frédéric Fabre, Julien Papaïx, et al. "Models of Plant Resistance Deployment." Annual Review of Phytopathology 59, no. 1 (2021): 125–52. http://dx.doi.org/10.1146/annurev-phyto-020620-122134.

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Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are
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Sriningsih, Riry, Muhammad Subhan, and Minora Longgom Nasution. "Analysis of torch deployment models." Journal of Physics: Conference Series 1317 (October 2019): 012013. http://dx.doi.org/10.1088/1742-6596/1317/1/012013.

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BUSHEHRIAN, OMID. "SOFTWARE PERFORMANCE ENGINEERING BY SIMULATED-BASED OBJECT DEPLOYMENT." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (2013): 211–21. http://dx.doi.org/10.1142/s0218194013500058.

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The object deployment of a distributed software has a great impact on its performance. In this paper an analytical model for performance evaluation of different object deployments, is presented. The key advantage of the proposed model over the traditional Queuing Network models is the usefulness in the deployment optimization when the search space is huge and automatic instantiation of Queuing performance models corresponding to an object deployment is costly. Since our model produces an optimal deployment corresponding to each input load separately, the runtime behavior of the software corres
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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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Vinayak, Kalluri, and Rambabu Kodali. "Benchmarking the quality function deployment models." Benchmarking: An International Journal 20, no. 6 (2013): 825–54. http://dx.doi.org/10.1108/bij-07-2011-0052.

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Thèses sur le sujet "Deployment models"

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Puntenney, Michael C. "Optimization models for military aircraft deployment." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27190.

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Amilitary aircraft deployment problem from the United States Transportation Command is modeled as a generalized transportation problem with side constraints and solved using a general purpose linear programming package. The model involves the assignment of military units and material to aircraft and th assignment of aircraft to missions in order to appraise the utility and to determine the assets required for preliminary military operation plans. A transformation of this model which aggregates variables relating to the early or late delivery of requirements is also described. A specialized alg
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Barreto, Gómez Tirso Leonardo. "Technological learning in energy optimisation models and deployment of emerging technologies /." Zürich, 2001. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14151.

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Duval, Thierry. "Models for design, implementation and deployment of 3D Collaborative Virtual Environments." Habilitation à diriger des recherches, Université Rennes 1, 2012. http://tel.archives-ouvertes.fr/tel-00764830.

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This work aims at providing some cues in order to address the essential requirements about the design of 3D Collaborative Virtual Environments (CVE). We have identified six essential topics that must be addressed when designing a CVE. For each of them, we present a state of the art about the solutions that can address this topic, then we show our own contributions: how we improve existing solutions and what are our new propositions. 1 - Choosing a model for the distribution of a CVE We need a distribution model to distribute as efficiently as possible the content of a CVE among all the nodes i
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Avital, Ittai. "Chance-constrained missile-procurement and deployment models for Naval Surface Warfare /." Diss., Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FAvital.pdf.

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John, Meenu Mary. "Design Methods and Processes for ML/DL models." Licentiate thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-45026.

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Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are com
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Theres, Michael J. "Models for comparing air-only and sea/air transportation of wartime deployment cargo." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA358943.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, December 1998.<br>"December 1998." Thesis advisor(s): R. Kevin Wood. Includes bibliographical references (p. 55-56). Also available online.
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Yang, Zhe. "Coexistence, Deployment and Business Models of Heterogeneous Wireless Systems Incorporating High Altitude Platforms." Doctoral thesis, Blekinge Tekniska Högskola, Avdelningen för elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00551.

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The increased demand for broadband communications has led to the rapid development of the conventional terrestrial and satellite wireless communications systems. One of the main challenges to next generation wireless systems is to deliver high-capacity and cost-efficient solutions to cope with an increasing usage of broadband services and applications. In the recent years, an emerging competitive system has attracted the attention for providing wireless broadband communications and other services based on quasi-stationary aerial platforms operating in the stratosphere known by high altitude pl
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Li, Pin. "A Systematic Methodology for Developing Robust Prognostic Models Suitable for Large-Scale Deployment." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593268220645085.

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Khajeh-Hosseini, Ali. "Supporting system deployment decisions in public clouds." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/3412.

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Decisions to deploy IT systems on public Infrastructure-as-a-Service clouds can be complicated as evaluating the benefits, risks and costs of using such clouds is not straightforward. The aim of this project was to investigate the challenges that enterprises face when making system deployment decisions in public clouds, and to develop vendor-neutral tools to inform decision makers during this process. Three tools were developed to support decision makers: 1. Cloud Suitability Checklist: a simple list of questions to provide a rapid assessment of the suitability of public IaaS clouds for a spec
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Islam, Kazi Mohammed Saiful. "Spatial dynamic queueing models for the daily deployment of airtankers for forest fire control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq35194.pdf.

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Livres sur le sujet "Deployment models"

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Puntenney, Michael C. Optimization models for military aircraft deployment. Naval Postgraduate School, 1989.

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Saranga, Haritha. Optimal deployment of parallel teams in new product development. Indian Institute of Management Bangalore, 2008.

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Theres, Michael J. Models for comparing air-only and sea/air transportation of wartime deployment cargo. Naval Postgraduate School, 1998.

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Walsh, William J. Carrier optimization launch algorithm: An optimization model to maximize the number of tactically tasked sorties under constraint restriction. Naval Postgraduate School, 1991.

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Dailey, Daniel J. Smart Trek: A model deployment initiative. Washington State Dept. of Transportation, 2001.

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Easan, Drury, Margolis, Robert M. (Robert Mark), and National Renewable Energy Laboratory (U.S.), eds. The Solar Deployment System (SolarDS) model: Documentation and sample results. National Renewable Energy Laboratory, 2009.

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Center, Turner-Fairbank Highway Research, ed. Development and field testing of Multiple Deployment Model Pile (MDMP). U.S. Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2000.

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Steinberg, Craig. Evaluation of the Commercial Vehicle Information Systems and Networks (CVISN) model deployment initiative. Federal Motor Carrier Safety Administration, Office of Research and Technology, 2002.

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Wisecarver, Michelle M. Deployment consequences: A review of the literature and integration of findings into a model of retention. U.S. Army Research Institute for the Behavioral and Social Sciences, 2006.

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Lloyd, Mark. Tactics of modern warfare: Rapid deployment in the 20th century. B. Trodd Pub. House, 1991.

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Chapitres de livres sur le sujet "Deployment models"

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Mendes, Felipe Cardeneti, Piotr Sarna, Pavel Emelyanov, and Cynthia Dunlop. "Infrastructure and Deployment Models." In Database Performance at Scale. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9711-7_7.

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AbstractAs noted in the previous chapter, many modern databases offer capabilities beyond “just” storing and retrieving data. But all databases are ultimately built from the ground up in order to serve I/O in the most efficient way possible. And it’s crucial to remember this when selecting your infrastructure and deployment model of choice.
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Towill, D. R., and J. E. Cherrington. "Learning Curve Models." In A Systems Approach to AMT Deployment. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3406-0_4.

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Singh, Pramod. "Model Deployment and Challenges." In Deploy Machine Learning Models to Production. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_2.

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Gomez Blanco, Daniel. "Sampling and Common Deployment Models." In Practical OpenTelemetry. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9075-0_10.

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Hansen, Ulrich Elmer, Cecilia Gregersen, Faith H. Wandera, Nina Kotschenreuther, and Rebecca Hanlin. "Centralised and decentralised deployment models." In Building Innovation Capabilities for Sustainable Industrialisation. Routledge, 2021. http://dx.doi.org/10.4324/9781003054665-4.

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Schillaci, Zachary. "On-Site Deployment of LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_23.

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AbstractAs consumer electronics and tensor computation for machine learning (ML) continue to advance, model execution and training become more accessible. NVIDIA introduced the RTX 4090 graphics cards, marketed initially as gamer-oriented products, in late 2022. Though relatively expensive for consumer use, their manufacturer’s suggested retail price (MSRP) of 1600 USD makes them affordable as a professional tool. These cards’ extensive video random access memory (vRAM), computational power comparable to last-generation flagship professional cards, and ability to use single-byte floats enable
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Singh, Pramod. "Machine Learning Deployment Using Docker." In Deploy Machine Learning Models to Production. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_4.

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Singh, Pramod. "Machine Learning Deployment Using Kubernetes." In Deploy Machine Learning Models to Production. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_5.

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Soldani, Jacopo, Uwe Breitenbücher, Antonio Brogi, Leonardo Frioli, Frank Leymann, and Michael Wurster. "Tailoring Technology-Agnostic Deployment Models to Production-Ready Deployment Technologies." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21637-4_1.

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Johnsen, Einar Broch, Rudolf Schlatte, and S. Lizeth Tapia Tarifa. "Deployment Variability in Delta-Oriented Models." In Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45234-9_22.

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Actes de conférences sur le sujet "Deployment models"

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Duessmann, Gabriel, and Adriano Fiorese. "Evaluating Serverless Function Deployment Models on AWS Lambda." In 27th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013279500003929.

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Eder, Johannes, Andreas Bahya, Sebastian Voss, Alexandru Ipatiov, and Maged Khalil. "From Deployment to Platform Exploration." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. ACM, 2018. http://dx.doi.org/10.1145/3239372.3239385.

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Weller, Marcel. "Automated Synchronization of Enterprise Architecture Models with Deployment Models." In MODELS Companion '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. ACM, 2024. http://dx.doi.org/10.1145/3652620.3688198.

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Andreasson, Johan, Naoya Machida, Masashi Tsushima, John Griffin, and Peter Sundström. "Deployment of high-fidelity vehicle models for accurate real-time simulation." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp1612478.

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Song, Hui, Rustem Dautov, Nicolas Ferry, Arnor Solberg, and Franck Fleurey. "Model-based fleet deployment of edge computing applications." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. ACM, 2020. http://dx.doi.org/10.1145/3365438.3410951.

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Stötzner, Miles, Floriment Klinaku, Robin Dominic Pesl, and Steffen Becker. "Enhancing Deployment Variability Management by Pruning Elements in Deployment Models." In UCC '23: IEEE/ACM 16th International Conference on Utility and Cloud Computing. ACM, 2023. http://dx.doi.org/10.1145/3603166.3632143.

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Ferry, Nicolas, and Phu H. Nguyen. "Towards Model-Based Continuous Deployment of Secure IoT Systems." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00093.

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Saidi, Salah Eddine, Nicolas Pernet, Yves Sorel, and Abir Ben Khaled. "Acceleration of FMU Co-Simulation On Multi-core Architectures." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124106.

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Teleman, Ylva, Pieter Dermont, Hak Jun Kim, and Kil Sang Jang. "Rankine Cycles, Modeling and Control." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124113.

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Kim, Eunkyeong, Tatsurou Yashiki, Fumiyuki Suzuki, Yukinori Katagiri, and Takuya Yoshida. "Thermal Deformation Analysis Using Modelica." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124121.

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Rapports d'organisations sur le sujet "Deployment models"

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Salunkhe, Sanjita Bharat. Intermittent Deployment of Branched CNN Models on Microcontrollers. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-915.

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Zinaman, Owen, Joseph Eto, Brooke Garcia, Jhi-Young Joo, Robert Jeffers, and Kevin Schneider. White Paper: Enabling Regulatory and Business Models for Broad Microgrid Deployment. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1906854.

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Foteinis, Spyros, and Phil Renforth. Realistic deployment scenarios/pathways that can be used to constrain Earth System models. OceanNETs, 2021. http://dx.doi.org/10.3289/oceannets_d6.2.

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Boemer, Jens, and Mobolaji Bello. Adaptive Protection and Validated Models to Enable Deployment of High Penetrations of Solar PV (PV-MOD). Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2477678.

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Simon, Pierre-Clement, Kyle Gamble, Arianna Pagani, et al. Deployment of BISON models of fuel restructuring at high burnup and related fission gas behavior in UO2. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2472822.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Keller, David P., Neha Mehendale, and Tronje P. Kemena. Analysis (report) of high- resolution modelling of efficacy, and regional impacts of selected ocean NETs close to the deployment sites. OceanNets, 2023. http://dx.doi.org/10.3289/oceannets_d4.3_v1.

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Many recent ocean modelling studies have demonstrated the added value of enhanced horizontal resolution, although it comes at a high computational cost. However, few modeling studies of ocean-based CDR have been done at high resolution. Here we assess the effects of model resolution on two simulated ocean-based CDR methods, unequilibrated ocean alkalinity enhancement (OAE) and the direct marine capture (DMC) of CO2 from seawater (with assumed permanent storage), in experiments with the FOCI Earth system model. To do this we utilized two FOCI configurations, one with a 1/2° ocean resolution and
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successfu
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Taucher, Jan, and Markus Schartau. Report on parameterizing seasonal response patterns in primary- and net community production to ocean alkalinization. OceanNETs, 2021. http://dx.doi.org/10.3289/oceannets_d5.2.

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We applied a 1-D plankton ecosystem-biogeochemical model to assess the impacts of ocean alkalinity enhancement (OAE) on seasonal changes in biogeochemistry and plankton dynamics. Depending on deployment scenarios, OAE should theoretically have variable effects on pH and seawater pCO2, which might in turn affect (a) plankton growth conditions and (b) the efficiency of carbon dioxide removal (CDR) via OAE. Thus, a major focus of our work is how different magnitudes and temporal frequencies of OAE might affect seasonal response patterns of net primary productivity (NPP), ecosystem functioning and
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Taucher, Jan, and Markus Schartau. Report on parameterizing seasonal response patterns in primary- and net community production to ocean alkalinization. OceanNETs, 2021. http://dx.doi.org/10.3289/oceannets_d5.3.

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We applied a 1-D plankton ecosystem-biogeochemical model to assess the impacts of ocean alkalinity enhancement (OAE) on seasonal changes in biogeochemistry and plankton dynamics. Depending on deployment scenarios, OAE should theoretically have variable effects on pH and seawater pCO2, which might in turn affect (a) plankton growth conditions and (b) the efficiency of carbon dioxide removal (CDR) via OAE. Thus, a major focus of our work is how different magnitudes and temporal frequencies of OAE might affect seasonal response patterns of net primary productivity (NPP), ecosystem functioning and
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