Engineering Scalable AI Pipelines for Large-Scale Data Platforms in Research and Industry

Abstract

The rapid expansion of artificial intelligence across research and industrial settings has intensified the need for scalable, reliable, and maintainable AI pipelines. As data volumes grow and models become more complex, traditional ad hoc workflows struggle to meet demands for performance, reproducibility, and operational stability. This study presents an engineering focused examination of scalable AI pipelines designed for large scale data platforms. The work synthesizes architectural patterns, methodological practices, and empirical evaluations that support robust model training, validation, and deployment. Emphasis is placed on modular pipeline design, distributed data processing, and automated lifecycle management. Experimental results demonstrate improvements in throughput, latency, and fault tolerance across representative workloads, illustrating how well engineered pipelines enable AI systems to transition from experimental prototypes to dependable production assets.