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Optimizing Data Pipeline Performance in ML Systems: Strategies for Eliminating Bottlenecks and Maximizing GPU UtilizationBy Amol Ashok Lele
Abstract: The inefficiency of the data pipeline is one of the most economically significant yet least understood challenges in machine learning infrastructure. High-performance GPU hardware costing several thousand dollars per device is now widely available, yet many organizations report utilization levels of only 40–60%, constrained not by compute capacity but by data loading, preprocessing, and memory-transfer overhead. This article examines the storage I/O bottleneck, the CPU preprocessing bottleneck, and the host-to-device …
Published in NOLEGEIN Journal of Information Technology & Management · Vol. 9, Issue 2, 2026 Read article →