Building and Deploying Machine Learning Services with AWS
DOI:
https://doi.org/10.59471/raia2025222Keywords:
Machine Learning in AWS, ML in Amazon, ML in the cloud, ML servicesAbstract
The explosive development of Artificial Intelligence in recent years came hand in hand with the development of cloud computing: Naturally, the availability of large volumes of data and configurable computing resources favored its evolution. It is therefore straightforward for cloud computing service providers to develop and provide Machine Learning (ML) and Artificial Intelligence (AI) services from their platforms. AWS being the leading global provider of cloud computing services, this paper details the implementation and deployment of the most prominent ML services provided by AWS. Reproducible examples of the services described are included.
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Borra, Praveen. (2022). “Exploring Microsoft Azure's Cloud Computing: A Comprehensive Assessment. International Journal of Advanced Research”. In Science, Communication and Technology (IJARSCT) Volume, 2.
Borra, Praveen. (2024). “A Survey of Google Cloud Platform (GCP): Features, Services, and Applications”. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) Volume, 4.
Louis Dorard, Mark D. Reid and Francisco J. Martin (Azure Machine Learning Team), (2016). AzureML: Anatomy of a machine learning service. In Conference on Predictive APIs and Apps (pp. 1-13). PMLR.
Fauss, M., Hao, J., Li, C., Palmer, M., & Choi, I. (2024). AutoSSD: A system for automated detection of similar speech responses in language tests.
Indla, R. K. (2021). An overview on amazon rekognition technology.
Kamlofsky, J. A. (2022). Computación en la Nube: Fundamentos, Críticas y Desafíos. Revista Abierta de Informática Aplicada, 6(2), 3-30.
Kamlofsky, J. A. (2024). Una Reseña Acerca de Servicios de Machine Learning en ambientes Cloud. Researchgate.net
Kodali, R. K., Shekhar, T., & Boppana, L. (2023). Automated Plagiarism Detection in Moodle. In TENCON 2023-2023 IEEE Region 10 Conference (TENCON) (pp. 176-181). IEEE.
Liberty, E., Karnin, Z., Xiang, B., Rouesnel, L., Coskun, B., Nallapati, R., ... & Smola, A. (2020, June). Elastic machine learning algorithms in amazon sagemaker. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 731-737).
Mell, P. (2011). The NIST Definition of Cloud Computing. Recommendations of the National Institute of Standards and Technology.
Movinuddin. (2023). HEALTHCARE TEXT ANALYTICS USING RECENT ML TECHNIQUES AND DATA CLASSIFICATION USING AWS CLOUD ML SERVICES (Doctoral dissertation, Middle Tennessee State University).
Oncu, M. H., & Kutlu, Y. (2024). Performance Comparison of Known AI Translation Tools. Tethys Env. Sci, 1(3), 117-126.
Poccia, D. (2016). AWS Lambda in Action: Event-driven serverless applications. Simon and Schuster.
Ranta, J. (2023). Testing AWS hosted Restful APIs with Postman.
Rivas, E. R. H. (2024). IMPLEMENTACIÓN DE APLICACIÓN WEB DE GESTIÓN DE COMERCIO EN AMAZON WEB SERVICES (AWS) PARA MULTISERVICIOS SANTA ELENA. Universidad de San Carlos de Guatemala.
Sarabia Jácome, D. F. (2020). Arquitectura de análisis de datos generados por el internet de las cosas IoT en tiempo real (Doctoral dissertation, Universitat Politècnica de València).
Sayers, Russell (2022). “Introducción al aprendizaje automático en AWS”, AWS. Curso en línea: https://www.coursera.org/learn/machine-learning-on-aws
Simon Julien (2020). “Building, training and deploying machine learning models with Amazon SageMaker”, Youtube. En línea: https://www.youtube.com/watch?v=sOUhLiI85sU
Sreeharsha, A., Kesapragada, S. M., & Chalamalasetty, S. P. (2022). Building chatbot using amazon lex and integrating with a chat application. Interantional journal of scientific research in engineering and management, 6(04), 1-6.
Wang, H., Yang, J., Liang, G., Lee, Y., & Cao, Z. (2024, August). Analyzing the Usability, Performance, and Cost-Efficiency of Deploying ML Models on BigQuery ML and Vertex AI in Google Cloud. In Proceedings of the 2024 8th International Conference on Cloud and Big Data Computing (pp. 15-25).
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