Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no learning. The goal of AI and ML is to try to simulate human thinking and understanding. AI and ML initiatives cannot however be realized unless the data processing layer has immediate access to, and a constant supply of, data.
The problem is that NAS solutions, often those designed for HPC environments, is what most organizations try to leverage as the AI/ML storage architectures. Legacy storage systems, like NAS, cannot support AI and ML workloads, because they were architected when spinning disk and slower networking technologies were the industry standard.
- NAS wasn’t architected to leverage today’s flash technology and can’t keep pace with the I/O demands, leaving GPUs starved for data
- NAS has no or very rudimentary Cloud Integration. Tiering to the cloud can play an integral role in AI and ML workloads
- NAS data protection schemes are expensive given the amount of data required to feed an AI/ML environment
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