Thematische Bibliographien / Deployment models

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Auswahl der wissenschaftlichen Literatur zum Thema „Deployment models“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 9. Juni 2025

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Zeitschriftenartikel zum Thema "Deployment models"

Ravi, Shankar Koppula. "Databricks MLflow." Journal of Scientific and Engineering Research 8, no. 11 (November 30, 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 Tracking, MLflow Projects, MLflow Models, and MLflow Model Registry. It highlights the platform's ability to address common challenges in machine learning projects, including experiment management, reproducibility, and model deployment across different environments. Furthermore, it delves into the crucial roles of MLflow Models and MLflow Model Registry in enabling flexible deployment options, such as batch, streaming, real-time, and edge deployments. These components also ensure centralized model management, compliance through audit logs, and facilitate collaboration among team members. In conclusion, the paper states that MLflow greatly contributes to overcoming the complexities of deploying real-time machine learning systems. It achieves this by offering streamlined workflows, centralized management, and comprehensive model deployment strategies. As a result, MLflow is seen as an invaluable resource for data scientists, machine learning practitioners, and researchers who are eager to enhance the efficiency and reproducibility of their machine learning projects.

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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 (July 13, 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 increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security.

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Howick, R. S., and M. Pidd. "Sales force deployment models." European Journal of Operational Research 48, no. 3 (October 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 (March 2021): 45–50. http://dx.doi.org/10.21276/ijircst.2021.9.2.8.

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Rimbaud, Loup, Frédéric Fabre, Julien Papaïx, Benoît Moury, Christian Lannou, Luke G. Barrett, and Peter H. Thrall. "Models of Plant Resistance Deployment." Annual Review of Phytopathology 59, no. 1 (August 25, 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 logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

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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 (March 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 corresponding to each input load should be profiled using simulation first. In this paper a translation scheme for generating the simulate-able Labeled Transition Systems (LTS) from scenarios is also presented. Moreover, two deployment algorithms (a GA-based and an INLP-based) are implemented and the results are compared.

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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 (October 18, 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 continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. Keywords—Machine Learning Operations (MLOps), Scalable AI Deployment, Continuous Integration and Continuous Deployment (CI/CD) for ML, ML Monitoring and Observability, Model Reproducibility, Model Versioning and Governance, Centralized Model Registry, Responsible AI Deployment, Ethical AI Practices, Enterprise MLOps

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Vinayak, Kalluri, and Rambabu Kodali. "Benchmarking the quality function deployment models." Benchmarking: An International Journal 20, no. 6 (October 21, 2013): 825–54. http://dx.doi.org/10.1108/bij-07-2011-0052.

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Perakis, Anastassions N., and Nikiforos Papadakis. "Fleet deployment optimization models. Part 1." Maritime Policy & Management 14, no. 2 (January 1987): 127–44. http://dx.doi.org/10.1080/03088838700000015.

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Dissertationen zum Thema "Deployment models"

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 algorithm which separates an instance of the model into subgroups of independent subproblems is also explored. Lastly, an integer rounding model is described which converts continious solutions to integer in order to facilitate implementation of the former models whith an existing post-solution processor. Excellent quality solutions are provided for problems involving nine routes, 80 movement requirements distributed across two cargo classes involving 200,000 short tons of freight, and 250 aircraft using four different aircraft types for each of 12 time periods. The problem, which has the potential of having over 10,000 variables, is reduced significantly using variable reduction and the aggregation transformation. The reduced problem requires approximately 1,000 variables and 300 constraints and solutions are obtainable in under 14 minutes using the General Algebraic Modeling System on an 80286-based personal computer

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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 involved in its execution, including the machines of the distant users. Our proposition is to allow CVE designers to mix in a same CVE the three main distribution models usually encountered: centralized on a server, totally replicated on each site, or distributed according to a hybrid distribution model. 2 - Choosing a model for the synchronization of these nodes To maintain consistency between all the nodes involved in the execution of a CVE, we must choose between a strong synchronization or a relaxed one, or an in-between solution. Our proposition is to manage some temporary relaxation of the synchronization due to network breakdowns, with several synchronization groups of users, making them aware of these network breakdowns, and to allow some shared objects to migrate from one site to another. 3 - Adapting the Virtual Environment to various hardware systems VR applications must be adapted to the software and to the hardware input and output devices that are available at run-time, in order to be able to deploy a CVE onto di fferent kinds of hardware and software. Our solution is the PAC-C3D software architectural model which is able to deal with the three main distribution modes encountered in CVE. 4 - Designing interaction and collaboration in the VE Expressing the interactive and collaborative capabilities of the content of a CVE goes one step beyond geometric modeling, by adding interactive and collaborative features to virtual objects. We propose a unified model of dialog between interactive objects and interaction tools, with an extension to Collada in order to describe interactive and collaborative properties of these interactive objects and interaction tools. 5 - Choosing the best metaphors for collaborative interactions Most of the time single-user interaction tools and metaphors are not adapted to off er effi cient collaboration between users of a CVE. We adapt some of these tools and metaphors to collaborative interactions, and we propose new really collaborative metaphors to enhance real multi-user collaborative interactions, with dedicated collaborative feedback. 6 - Embedding the users' physical workspaces within the CVE Taking into account users' physical workspaces makes it possible to adapt a CVE to the hardware input and output devices of the users, and to make them aware of their physical limitations and of those of the other users, for better interaction and collaboration. We propose the Immersive Interactive Virtual Cabin (IIVC) concept to embed such 3D representations in CVE.

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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 complex. Regardless of the level of expertise and/or access to data scientists, companies in all embedded systems domain struggle to build high-performing models due to a lack of established and systematic design methods and processes. Objective: The overall objective is to establish systematic and structured design methods and processes for the end-to-end process of developing, deploying and successfully evolving ML/DL models. Method: To achieve the objective, we conducted our research in close collaboration with companies in the embedded systems domain using different empirical research methods such as case study, action research and literature review. Results and Conclusions: This research provides six main results: First, it identifies the activities that companies undertake in parallel to develop, deploy and evolve ML/DL models, and the challenges associated with them. Second, it presents a conceptual framework for the continuous delivery of ML/DL models to accelerate AI-driven business in companies. Third, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on how to integrate, deploy and operationalize ML/DL models. Fourth, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge. These architectural alternatives range from a centralized architecture that prioritizes (re)training in the cloud to a decentralized architecture that prioritizes (re)training at the edge. Fifth, it identifies key factors to help companies decide which architecture to choose for deploying ML/DL models. Finally, it explores how MLOps, as a practice that brings together data scientist teams and operations, ensures the continuous delivery and evolution of models.

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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 platforms (HAPs), and located 17-22 km above the earth surface. This solution has been described by the International Telecommunication Union (ITU) as "a new and long anticipated technology that can revolutionize the telecommunication industry''. The HAP systems provide important advantages such as low cost, high elevation angles, low propagation delay, easy and incremental deployment, flexibility in operation, broad coverage, broadcast and broadband capability, ability to move around in emergency situations, etc. Therefore, they have been proposed by ITU for the provision of fixed, mobile services and applications, e.g. the third generation (3G) services licensed by ITU and backbone link for terrestrial networks in remote areas. This thesis explores and investigates the wireless communication and techno-economic performance of terrestrial systems and HAPs. An overview of research and development on aerial platforms worldwide is given. Coexistence performance and techniques of heterogeneous systems to provide broadband wireless communications based on Worldwide Interoperability Microwave Access (WiMAX) are investigated. A heterogeneous scenario is developed to examine the coexistence performance of heterogeneous systems. The capacity and deployment aspects of HAPs are analyzed, and further compared with terrestrial Universal Mobile Telecommunications Systems (UMTS) through techno-economic studies including a proposed partnership based business model for HAPs. Performance of wireless sensor network applications via HAPs is also investigated, and shows the high potential of HAPs for large-area and long-endurance surveillance and emergency applications. The thesis shows that communications from the aerial platforms provide the best features of both terrestrial and satellite systems. HAPs can effectively coexist in a heterogeneous radio environment, and are competitive solutions in urban and suburban scenarios in terms of capacity, coverage and business perspective. This makes HAP a viable competitor and complement to conventional terrestrial infrastructures and satellite systems.

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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 specific IT system. 2. Benefits and Risks Assessment tool: a spreadsheet that includes the general benefits and risks of using public clouds; this provides a starting point for risk assessment and helps organisations start discussions about cloud adoption. 3. Elastic Cost Modelling: a tool that enables decision makers to model their system deployment options in public clouds and forecast their costs. These three tools collectively enable decision makers to investigate the benefits, risks and costs of using public clouds, and effectively support them in making system deployment decisions. Data was collected from five case studies and hundreds of users to evaluate the effectiveness of the tools. This data showed that the cost effectiveness of using public clouds is situation dependent rather than universally less expensive than traditional forms of IT provisioning. Running systems on the cloud using a traditional 'always on' approach can be less cost effective than on-premise servers, and the elastic nature of the cloud has to be considered if costs are to be reduced. Decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Performing upfront cost modelling is beneficial as there can be significant cost differences between different cloud providers, and different deployment options within a single cloud. During such modelling exercises, the variations in a system's load (over time) must be taken into account to produce more accurate cost estimates, and the notion of elasticity patterns that is presented in this thesis provides one simple way to do this.

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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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Bücher zum Thema "Deployment models"

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

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Saranga, Haritha. Optimal deployment of parallel teams in new product development. Bangalore: 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. Monterey, Calif: 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. Monterey, Calif: Naval Postgraduate School, 1991.

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Dailey, Daniel J. Smart Trek: A model deployment initiative. [Olympia, Wash.]: 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. Golden, Colo: 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). McLean, VA: 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. Washington, DC: 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. Arlington, Va: 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. London: B. Trodd Pub. House, 1991.

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Buchteile zum Thema "Deployment models"

Mendes, Felipe Cardeneti, Piotr Sarna, Pavel Emelyanov, and Cynthia Dunlop. "Infrastructure and Deployment Models." In Database Performance at Scale, 131–55. Berkeley, CA: 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, 57–75. London: 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, 55–66. Berkeley, CA: 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, 179–202. Berkeley, CA: 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, 71–87. London: 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, 205–11. Cham: 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 a pair of them to train, fine-tune, and run on-premises Large Language Models (LLMs) with up to 7 billion parameters per card. Until this release, such a feat would have required data center-level equipment. Although the RTX 4090 and H100 GPU represent a qualitative step forward, iterative improvements combined with the speculated lowering of computational precision to half-byte floats could make larger models even more accessible for on-premises use. This development might, in one aspect, lower the entry barrier for cyberattackers, simplifying the process for advanced persistent threats (APTs) to camouflage their activities amidst unsophisticated attackers or those employing generative LLMs for non-malicious purposes. Conversely, as an alternative to cloud-hosted models, on-site LLMs may limit the possibility of private information leakage or model poisoning while offering specialized capabilities for legitimate users.

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Singh, Pramod. "Machine Learning Deployment Using Docker." In Deploy Machine Learning Models to Production, 91–126. Berkeley, CA: 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, 127–46. Berkeley, CA: 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, 1–24. Cham: 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, 304–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45234-9_22.

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Konferenzberichte zum Thema "Deployment models"

Duessmann, Gabriel, and Adriano Fiorese. "Evaluating Serverless Function Deployment Models on AWS Lambda." In 27th International Conference on Enterprise Information Systems, 740–47. 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. New York, NY, USA: 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, 136–41. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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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Berichte der Organisationen zum Thema "Deployment models"

Salunkhe, Sanjita Bharat. Intermittent Deployment of Branched CNN Models on Microcontrollers. Ames (Iowa): Iowa State University, May 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), December 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, November 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), September 2024. http://dx.doi.org/10.2172/2477678.

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Simon, Pierre-Clement, Kyle Gamble, Arianna Pagani, Ian Ferguson, Daniel Schwen, Logan Harbour, Larry Aagesen Jr, 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), September 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, December 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-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.

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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, November 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 the other with a 1/10° ocean nest in the N. Atlantic. Both configurations were run in a series of “paired” experiments with identical climate forcing and CDR deployments. We show that model resolution does appear to matter when simulating OAE and DMC. For OAE, parameterization of physical processes in the coarse resolution version of the model appears to overestimate how long alkalized waters stay in contact with the atmosphere and where they are transported. This results in large differences in OAE efficacy with almost twice as much carbon sequestered when the model resolution is coarse. For the DMC simulations, at one site there were clear differences in the compensating CO2 flux induced by DIC removal, which was again higher with a coarse resolution, while at the other site variability was high and differences were difficult to determine. At both DMC sites there were clear differences in circulation with the two model resolutions, and thus on downstream biogeochemistry. We suggest that well resolving ocean physics may be necessary to best calculate unequilibrated OAE and DMC efficacies and side effects. These results should be confirmed using other models and with different resolutions.

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 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 successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.

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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, November 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 biogeochemical cycling. With our study we aimed at identifying a parameterization of how magnitude and frequency of OAE affect net growth rates, so that these effects could be employed for Earth System Modell applications. So far we learned that a meaningful response parameterization has to resolve positive and negative anomalies that covary with temporal shifts. As to the intricacy of the response patterns, the derivation of such parameterization is work in progress. However, our study readily provides valuable insights to how OAE can alter plankton dynamics and biogeochemistry. Our modelling study first focuses at a local site where time series data are available (European Station for Time series in the Ocean Canary Islands ESTOC), including measurements of pH, concentrations of total alkalinity, dissolved inorganic carbon (DIC), chlorophyll-a and dissolved inorganic nitrogen (DIN). These observational data were made available by Andres Cianca (personal communication, PLOCAN, Spain), Melchor Gonzalez and Magdalena Santana Casiano (personal communication, Universidad de Las Palmas de Gran Canaria). The choice of this location was underpinned by the fact that the first OAE mesocosm experiment was conducted on the Canary Island Gran Canaria, which will facilitate synthesizing our modelling approach with experimental findings. For our simulations at the ESTOC site in the Subtropical North Atlantic we found distinct, non-linear responses of NPP to different temporal modes of alkalinity deployment. In particular, phytoplankton bloom patterns displayed pronounced temporal phase shifts and changes in their amplitude. Notably, our simulations suggest that OAE can have a slightly stimulating effect on NPP, which is however variable, depending on the magnitude of OAE and the temporal mode of alkalinity addition. Furthermore, we find that increasing alkalinity perturbations can lead to a shift in phytoplankton community composition (towards coccolithophores), which even persists after OAE has stopped. In terms of CDR, we found that a decrease in efficiency with increasing magnitude of alkalinity addition, as well as substantial differences related to the timing of addition. Altogether, our results suggest that annual OAE during the right season (i.e. physical and biological conditions), could be a reasonable compromise in terms of logistical feasibility, efficiency of CDR and side-effects on marine biota. With respect to transferability to global models, the complex, non-linear responses of biological processes to OAE identified in our simulations do not allow for simple parameterizations that can easily adapted. Dedicated future work is required to transfer the observed responses at small spatiotemporal scales to the coarser resolution of global models.

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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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