AI-Driven Natural Language Processing Models Deployed on Scalable Cloud Architectures

Authors

  • Phani Rohitha Kaza

Keywords:

Artificial Intelligence; Natural Language Processing; Cloud Computing; Scalable Architectures; Transformer Models; Distributed Systems; AI Deployment.

Abstract

The active development of Artificial Intelligence (AI) has contributed greatly to the Natural Language Processing (NLP) and nowadays machines can read, comprehend, and create human speech with the most extraordinary precision. At the same time, scalable cloud models have become a building block towards implementing computationally intensive AI-based NLP models at scale. The current paper will provide an in-depth analysis of AI-based NLP applications implemented on the scalable cloud infrastructure basing on the model architecture, operational performance, and applicability to practice. The suggested model will combine transformer-based NLP systems with cloud-based technologies, including construction, auto-scaling, and distributed storage, to ensure high performance, flexibility, and cost effectiveness. Experimental analysis shows that response time, throughput and scalability is better than that of traditional on- premise deployment. But real constraints like privacy of data, the variable latency, price unpredictability and reliance on the cloud vendor continue to be major setbacks. The paper ends with a conclusion about the research perspectives and future research directions, such as edge-cloud hybrid NLP implementation, optimizing model resource consumption, federated learning to protect privacy, and orchestrating resources to increase the resilience and sustainability of cloud-based NLP systems.

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Published

31.12.2023

How to Cite

Phani Rohitha Kaza. (2023). AI-Driven Natural Language Processing Models Deployed on Scalable Cloud Architectures. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 1008 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8042

Issue

Vol. 11 No. 11s (2023)

Section

Research Article

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