International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
PRODUCTIONIZING LARGE LANGUAGE MODELS: CHALLENGES AND ARCHITECTURAL CONSIDERATIONS Venkata Raj Kiran Kollimarla ---------------------------------------------------------------------------***----------------------------------------------------------------------ABSTRACT: Large Language Models (LLMs) have changed the way natural language processing is done by demonstrating unprecedented skills in understanding, creating, translating, and summarizing language. LLMs have the potential to revolutionize many areas, such as customer service, healthcare, creative writing, and education. However, using LLMs in real-world situations is challenging because of issues with computing power, model size, privacy, bias, scale, and how fast the models can be interpreted. This article examines these issues in great detail, using research and case studies from real-world applications. It also talks about important design ideas for putting LLMs into production, such as microservices architecture, containerization, serverless computing, federated learning, continuous integration/deployment, model serving platforms, and distributed computing. By following these guidelines, businesses can create significant value by leveraging LLMs in production systems that are scalable, efficient, and reliable while effectively addressing the common challenges encountered in this context.
Keywords: Large Language Models (LLMs), Production Challenges, Architectural Considerations, Scalability, Deployment Strategies
INTRODUCTION: Large Language Models (LLMs) are cutting-edge AI systems that have been taught on huge amounts of textual data to understand and write text that sounds like it was written by a person [1]. These models have shown remarkable skills in many natural language processing tasks, such as understanding language, creating writing, translating, and summarizing [2]. When it comes to jobs like question answering, text completion, and language translation [3], OpenAI's GPT-3 model, which has 175 billion parameters, does very well. In the same way, Google's BERT model, which has 340 million parameters, has shown big gains in recognizing named entities, analyzing mood, and sorting text [4].
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