NLPOps: A Comprehensive Framework for Secure Development and Scalable Deployment of Multifaceted LLMs in Generative AI

Abstract

The burgeoning field of Generative AI relies heavily on Multifaceted Large Language Models (LLMs) to achieve tasks like NER, Document summary, Translation, Text classification, Sentiment Analysis, Text generation, Question & Answer and Document Similarity. However, developing and deploying these complex models remains a challenge due to concerns about security and scalability. This paper proposes "NLP Ops: A Comprehensive Framework for Secure Development and Scalable Deployment of Multifaceted LLMs in Generative AI." This framework addresses these challenges by combining best practices in secure software development, distributed computing, and operational monitoring. The framework encompasses secure data handling, adversarial training, containerization, distributed infrastructure, and comprehensive monitoring for performance and security. Results demonstrate that NLP Ops [mention key findings, e.g., improves security by 98%, increases processing speed by 97%. This paper contributes to the advancement of NLP Ops by providing a practical and secure approach to developing and deploying Multifaceted LLMs, paving the way for wider adoption of Generative AI technologies.