Research on Data Service Platforms for Large Language Model Applications

With the widespread application of large language models (LLMs) such as the GPT series and BERT in Natural Language Processing (NLP), constructing efficient data service platforms to support their large-scale applications and optimize model performance has become a hot research topic. This paper aims to investigate the design and optimization of data service platforms for LLM applications, exploring the challenges and solutions in platform architecture, data storage management, and data processing optimization. First, the data requirements for LLMs, including data volume, quality, real-time processing, and scalability, are analyzed, and the application of multimodal data fusion and distributed data processing technologies is proposed. Secondly, a modular data service platform architecture is designed, addressing issues such as storage, management, interfaces, and API design. Experimental results demonstrate that optimizing data processing workflows and platform architecture significantly enhances LLM training and inference efficiency, driving the implementation of large-scale data-driven AI applications. Finally, the paper discusses the technical challenges in platform design and offers further optimization suggestions, providing theoretical support and practical guidance for the widespread application of LLMs.