SANTA CLARA, Calif. – Artificial Intelligence and Machine Learning applications are becoming ubiquitous in companies of all sizes, across all industries, and require significant up-front investment in hardware. Along with initial costs, however, come ongoing operational costs of running these data-intensive workloads. Experts at Quobyte® Inc., a leading developer of modern storage system software, offer the following tips for reducing the operational expenses of AI/ML infrastructures. Smart storage AI/ML workloads have different performance profiles, including the high-throughput, low-latency requirements in the model training stage, plus there can be large-block sequential, small-block random, or mixed general workloads during the ingest or other…