Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit

Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit | CIO

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The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training.

For many nascent AI projects in the prototyping and experimentation phase, the cloud works just fine. But companies often discover that as data sets grow in volume and AI model complexity increases, the escalating cost of compute cycles, data movement, and storage can spiral out of control. Called data gravity, it’s the cost and workflow latency of bringing large data sets from where they’re created to where compute resources reside. It has caused many companies to consider moving their AI training from the cloud back to an on-premises data center that is data-proximate.

Hybrid is a perfect fit for some AI projects

There’s an alternative worth exploring — one that avoids forcing an either/or choice around cloud and on-premises. A hybrid cloud infrastructure approach enables companies to take advantage of both environments. In this case, organizations can utilize on-premises infrastructure for their on-going “steady state” training demands, supplemented with cloud services for temporal spikes or unpredictable surges that exceed that capacity.

“The saying: ‘Own the base, rent the spike’ captures the essence of this situation,” says

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