Scalable NLP in the Enterprise: Training Transformer Models on Distributed Cloud GPUs
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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