AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: BOGDAN MURSA, MÁTYÁS KUTI-KRESZÁCS, CRISTIANA MOROZ-DUBENCO, FLORENTIN BOTA.
DOI: 10.24193/subbi.2023.2.04
Published Online: 2023-12-22
pp. 53-68
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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.
Received by the editors: 29 June 2023.
2010 Mathematics Subject Classification 68T01.
1998 CR Categories and Descriptors. D.2.11 [Software Engineering]: Software Architectures – Patterns (e.g., client/server, pipeline, blackboard); I.2.1 [Artificial Intelligence]: Applications and Expert Systems – Industrial automation.
Keywords and phrases: Artificial Intelligence, Automation, Optimization.
