End-to-End Machine Learning Pipelines for Mobile and Web Apps: Backend Infrastructure and API Development

The integration of machine learning (ML) into mobile and web applications has redefined user experiences by enabling intelligent, real-time, and personalized functionalities. This study explores the design and implementation of an end-to-end ML pipeline, focusing on backend infrastructure and API development to support scalable and efficient deployment. The proposed pipeline encompasses data preprocessing, model training, backend setup using containerized environments, and API optimization for real-time predictions. Results demonstrate significant improvements in system performance, scalability, and user engagement, with reduced latency and enhanced reliability. Key challenges, including model optimization and resource efficiency, are addressed, providing a comprehensive framework for ML-powered application development. This research offers actionable insights for leveraging intelligent technologies in modern digital ecosystems.