Why Your AI Workflow Needs Software-based Secondary Storage

Ultimately, you won’t have the budget to keep all your data for deep learning within your primary storage tier.

The need to keep your performance compute and storage tier fresh for analysis will necessitate managing your long-tail data storage intelligently to a secondary and even tertiary storage tier.

Enterprises that tap the real power of AI will have ready access to data no matter how much they have.

To be one of these AI-data-management superstars, you know that you have to avoid legacy IT infrastructure which just isn’t going to cut it.

And you know that any large-scale data management solution must avoid new complexities.

The challenge is that many of the options on the market today are old solutions, rebranded for the cloud era, that fundamentally behave as new infrastructure.

One of the chief obstacles to executing artificial intelligence is managing massive volumes of unstructured data.

The trick is keeping this data intact to your AI/ML infrastructure without adding silos and layers of complexity.

Secondary, scale-out, and cloud storage components are essential to large-scale AI projects, but they shouldn’t be independent silos that make it challenging to tier and recall data sets as needed.

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