Scalable NLP in the Enterprise: Training Transformer Models on Distributed Cloud GPUs
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Keywords:
transformers, BERT, distributed training, cloud GPUs, customer service automationAbstract
This paper explores the large-scale deployment of transformer-based models, specifically BERT and its variants, for enterprise applications in customer service automation and legal document processing. It presents an in-depth analysis of strategies for training such models on distributed cloud-based GPU infrastructures, highlighting optimizations in data parallelism, model parallelism, and input pipeline design. Leveraging frameworks such as TensorFlow and PyTorch, along with orchestration via Kubernetes and Horovod, the paper examines techniques to achieve scalability, fault tolerance, and efficient resource utilization. Additionally, it discusses domain-specific pretraining, fine-tuning pipelines, and inference acceleration for real-time enterprise workloads. Empirical results demonstrate the feasibility and performance trade-offs of scaling transformer architectures in production environments. The findings underscore the practical implications of marrying cutting-edge NLP with robust cloud-native infrastructure to drive operational efficiency in data-intensive domains.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.