Abstract

The rapid growth of web and mobile platforms has driven the demand for real-time machine learning (ML) applications capable of delivering low-latency, high-throughput, and scalable performance. This study explores the design and evaluation of scalable backend solutions tailored for such applications. By analyzing various architectural frameworks, database systems, load-balancing strategies, and model-serving frameworks, the study identifies serverless computing as the most efficient approach, offering unmatched scalability, resource optimization, and fault tolerance. Redis emerged as the optimal database for latency-critical tasks, while TensorFlow Serving demonstrated superior inference accuracy and low latency for real-time model deployment. The findings emphasize the importance of combining modern architectures with adaptive technologies to achieve robust and cost-effective backend infrastructures. This research provides actionable insights for developers and stakeholders seeking to optimize real-time ML solutions for diverse use cases