AI’s storage challenge is really an operational one
Enterprise infrastructure has always adapted as scale increased. Virtualization tackled server sprawl, cloud computing reduced the need to provision physical resources for every application, and automation made increasingly complex environments manageable. Artificial intelligence presents a different kind of scaling problem.
The discussion around enterprise AI has largely centered on models, GPUs, and inference performance, but those technologies represent only a fraction of what organizations must operate. Every production AI deployment creates a continuous flow of data that must be ingested, protected, moved, analyzed, retained, governed, and eventually archived.
Those activities place demands on infrastructure that are very different from the workloads storage systems were originally designed to support.
Director of Product Marketing at Scality.
This is becoming increasingly apparent as organizations move beyond pilot projects. AI is no longer a single workload running on isolated infrastructure. A single application may include high-speed storagefor model training, object storage for inference...
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