Using Azure Maching Learning Cloud Technology for Electric Machines Optimization

Pliuhin, Vladyslav; Pan, Mykola; Yesina, Valeriya; Sukhonos, Maria

doi:10.1109/infocommst.2018.8632093

Cited by 10 publications

(5 citation statements)

References 1 publication

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“…As a solution for the task of designing the complex systems that an electrical machine consists of, [21] employ AzureML as a means of optimization and best candidate selection. That is, the platform was used to compare two searching algorithms, namely Boosted Decision Tree and Multiclass Neural Network, in order to predict the best configuration of an electric motor according to the maximum efficiency.…”

Section: Advances In Automated Ai Trainingmentioning

confidence: 99%

“…Since we need high scalability and low latency and, most importantly, an architecture that is suitable for productionlevel deployments, our choice is based on the same reasons as presented in [23]. Similar to the work presented in [21,27,8], we need to train multiple AI models and choose the one that yields the best results. And, finally, the most notable characteristic of the AzureML platform that motivates the choice of AzureML for all of the presented solutions, including our own, is the possibility of being used even by non ML-specialized developers.…”

Section: Advances In Automated Ai Trainingmentioning

confidence: 99%

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Facilitating Model Training With Automated Techniques

Mursa,

Kuti-Kreszács,

Moroz-Dubenco

et al. 2023

Studia UBB Informatica

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Automating artificial intelligence (AI) model training has emerged as a significant challenge in the field of automation. The complete pipeline from raw data to model deployment poses the need to define robust processes that ensure the efficiency of the services that expose the models. This paper introduces a generic architecture for automating data preparation, training of models, selection of models, and deployment of models as web services for third-party consumption using Microsoft Azure Machine Learning’s (AzureML) CI/CD tools. We conducted a practical experiment utilizing AzureML pipelines with predefined and custom modules, demonstrating its readiness for integration into any production application. We also successfully integrated this architecture into a real-world product designed for industrial forecasting. This practical implementation demonstrates the effectiveness and adaptability of our approach, indicating its potential to address diverse training needs. Keywords and phrases: Artificial Intelligence, Automation, Optimization.

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Section: Advances In Automated Ai Trainingmentioning

confidence: 99%

Section: Advances In Automated Ai Trainingmentioning

confidence: 99%

Facilitating Model Training With Automated Techniques

Mursa,

Kuti-Kreszács,

Moroz-Dubenco

et al. 2023

Studia UBB Informatica

View full text Add to dashboard Cite

show abstract

“…Considering the characteristics and benefits of the technologies mentioned above, a data-driven design optimization platform can be developed based on industrial big data (material data and manufacturing data) and available cloud services (cloud computing [14,161] and manufacturing). In the future, the optimal design of an electromagnetic device should include the best topology, shape, dimension, and material, and the most appropriate manufacturing process.…”

Section: Data-driven Design Optimization Based On Cloud Servicesmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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“…Data Factory takes care of all such processes to make it automate and thus serve the cause. It would orchestrate the process in a manageable and organizable manner [11].…”

Section: Azure Working Mechanismmentioning

confidence: 99%

The Dynamic Computational Model and the New Era of Cloud Computation Using Microsoft Azure

2020

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The new era of computation has given new directions for advancement and sophistication. The meaning of computation has made a paradigm shift from device and location orientation to the distributed level. Storage has become cheaper. Multiple technologies and their interconnections and their wide range of services have made this planet to be more sophisticated and more flexible in computing. Service orientation based computing of cloud has achieved a huge success. Data centers has already existing even before the emergence of cloud technology, but cloud makes these data centers to communicate, between these data centers and allocate the resources properly and functioning appropriately using a network and to used these resources and maintain them properly. Most of the cloud services providers have their own Data center. 'Azure' online cloud platform provided Microsoft provides cloud services and resources to the end-user. Its pricing mechanism is economical and flexible. One can learn Azure very easily and Micro soft company provides Azure certifications for the one who qualifies with international standards.

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Using Azure Maching Learning Cloud Technology for Electric Machines Optimization

Cited by 10 publications

References 1 publication

Facilitating Model Training With Automated Techniques

Facilitating Model Training With Automated Techniques

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

The Dynamic Computational Model and the New Era of Cloud Computation Using Microsoft Azure

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