Building Scalable Airlines Reservation Systems: A Microservices Approach Using AI and Deep Learning for Enhanced User Experience

This study investigates the design and implementation of a scalable airline reservation system through a microservice architecture with AI-based personalization and real-time updates. This architecture, on the other hand, breaks the various core functionalities into independent services so as to deliver a statement of flexibility, scalability, and fault tolerance. This system uses ASP.NET as its front end for providing dynamic and user-friendly interfaces for applications while SQL Server will act as the back-end database to store data reliably and manage it effectively. Personalization and optimizations like flight recommendations and fraud detections are made using AI-based frameworks like TensorFlow and PyTorch. The real-time airlines databases and APIs keep flight updates accurate and fresh for the users. A case study prototype simulation where immediate user experience through notifications and personalized flight recommendations can be judged based on the performance of the proposed system pertaining to load handling, latency, and fault-tolerance simulation as compared to traditional reservation systems demonstrates such performance. The former win will subsequently reveal superior scalability, with better optimized response times and resilience at peak times of traffic durability. Also, instantaneous notifications and personalized flight recommendations improve user satisfaction through deep learning models. It can thus be concluded that this system improves significantly in scalability, efficiency, and user engagement by providing very solid and future-proof solutions for modern airline reservations. On one hand by microservices usage and AI, the system provides a pretty much efficient, fault-tolerant, and scalable replacement over conventional reservation frameworks.