End of Studies Project: Retrieval-Augmented Generation (RAG) System for Managing Invoices and Orders in a company:Developed a Retrieval-Augmented Generation (RAG) system to manage enterprise invoices and orders, enhancing data retrieval and user query response accuracy.Implemented advanced information extraction from invoice images, utilizing OCR techniques to extract e informations from an invoice and transform it into a structured data(JSONformat).Engineered a robust ETL (Extract, Transform, Load) data pipeline to streamline data processing, storage, and retrieval in AWS.Hosted the entire solution on an EC2 instance, ensuring high availability and scalability.Enhanced user experience by providing generative responses to specific queries, improving efficiency in handling invoice and order information.Skills:Data Pipeline DevelopmentAWS (EC2, S3)Retrieval-Augmented Generation (RAG)ETL Processes

Develop an application that helps to predict potential failures in an aircraft's component.The application uses machine learning models to analyze data related to the aircraft's components, performance, and historical failure patterns. The goal is to proactively identify potential issues before they occur, allowing for timely maintenance and reducing the risk of unexpected failures during flights.