Zobrazeno 1 - 10
of 434
pro vyhledávání: '"MLOps"'
Autor:
Neda Peyrone, Duangdao Wichadakul
Publikováno v:
IEEE Access, Vol 12, Pp 142524-142541 (2024)
In the artificial intelligence (AI) era, data has become increasingly essential for learning and analysis. AI enables automated decision-making that may lead to violation of the General Data Protection Regulation (GDPR). The GDPR is the data protecti
Externí odkaz:
https://doaj.org/article/8d3fab343a704e4c996c7c2c1016a186
Publikováno v:
IEEE Access, Vol 12, Pp 4301-4314 (2024)
Federated learning (FL) is a decentralized machine learning (ML) method that enables model training while preserving privacy. FL is gaining attention because it avoids data transfer to the server, facilitating the decentralized learning of the tradit
Externí odkaz:
https://doaj.org/article/219a9df388fd432298a5ccc917ae0258
Publikováno v:
Internet Research, 2023, Vol. 33, Issue 7, pp. 168-205.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/INTR-10-2022-0769
Publikováno v:
SoftwareX, Vol 26, Iss , Pp 101648- (2024)
Data Science has emerged as a vital discipline applicable across numerous industry sectors. However, achieving reproducibility in this field remains a challenging and unresolved problem. Additionally, transitioning trained models from development to
Externí odkaz:
https://doaj.org/article/a49570ebfaa248cea3edf950708dc999
Autor:
Taras Ustyianovych, Nadiia Kasianchuk, Halina Falfushynska, Solomiia Fedushko, Eduard Siemens
Publikováno v:
Proceedings of the International Conference on Applied Innovations in IT, Vol 11, Iss 2, Pp 81-90 (2023)
The availability of robust end-to-end ML processes plays a crucial role in delivering an accurate and reliable system for real-time text data inference. In this paper, we present an approach to building machine learning operations (MLOps) and an obse
Externí odkaz:
https://doaj.org/article/70543692246b4de99cb32cbf020334a5
Publikováno v:
Sensors, Vol 24, Iss 16, p 5310 (2024)
The Industrial Internet of Things has enabled the integration and analysis of vast volumes of data across various industries, with the maritime sector being no exception. Advances in cloud computing and deep learning (DL) are continuously reshaping t
Externí odkaz:
https://doaj.org/article/9a3d2f967a3d440abc389c03e8086b8d
Autor:
Prerna Singh
Publikováno v:
Data Science and Management, Vol 6, Iss 3, Pp 144-157 (2023)
Artificial intelligence (AI) relies on data and algorithms. State-of-the-art (SOTA) AI smart algorithms have been developed to improve the performance of AI-oriented structures. However, model-centric approaches are limited by the absence of high-qua
Externí odkaz:
https://doaj.org/article/8e693534bf4448dc99d2e0f5465995c4
Publikováno v:
International Journal of Production Management and Engineering, Vol 11, Iss 2, Pp 179-186 (2023)
The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. Al
Externí odkaz:
https://doaj.org/article/6fd1b4588e38402e9b2cc9b1cf2bc69d
Publikováno v:
Frontiers in Public Health, Vol 12 (2024)
BackgroundThe healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are pri
Externí odkaz:
https://doaj.org/article/362621c330c4450ba660ff0dfe9e2d30
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an ene
Externí odkaz:
https://doaj.org/article/e46e76f5d7334b8a9ccd72885f280796