Leveraging AI, ML, and LLMs for Predictive Trade Analytics and Automated Metadata Management (Published)
The integration of Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) has revolutionized trade data analytics and metadata management within cloud environments. The implementation of advanced predictive models, coupled with sophisticated cloud architectures, enables organizations to process vast amounts of heterogeneous data while delivering real-time insights for strategic decision-making. The architecture encompasses multiple layers of data processing, including event-driven systems for trade pattern recognition, automated metadata extraction, and intelligent classification mechanisms. Through the deployment of specialized ML models, including time series analysis, natural language processing, and graph neural networks, the system achieves enhanced prediction accuracy across diverse trading scenarios. The incorporation of AI-driven metadata management strengthens data governance through automated lineage tracking, compliance monitoring, and dynamic access control. Performance optimization techniques, including adaptive model selection and dynamic resource allocation, ensure sustained system efficiency. The implementation demonstrates significant improvements in processing speed, prediction accuracy, and resource utilization while maintaining robust security and compliance frameworks.
Keywords: artificial intelligence in trade analytics, automated metadata management, cloud-based predictive systems, machine learning optimization, real-time data processing