What storage considerations are associated with artificial intelligence?
Artificial intelligence (AI) is a common sight among enterprises today. While it's not all that new anymore, it's still a very cool asset to have. Thus, it's no wonder that everyone is jumping on the bandwagon. However, before you have too much fun with it, you must make yourself familiar with the storage requirements that come with it.
Organisations are in need of a storage strategy that can accommodate for the data AI creates. This strategy needs to be cost-effective and simple to manage for your benefit, but also needs to meet the demands of AI (which are pretty high). Given that neither side wants to back down, finding a solution to strike a balance is imperative.
What to bear in mind
Scalability is perhaps the most obvious demand that springs to mind. Huge data sets are needed to train AI. For example, AI-powered image detection may require millions of images in its training. Once the AI starts learning, it will also start generating data of its own. As these data sets are essential to developing the best possible algorithm, a storage system is required that can scale without limits.
Furthermore, losing the data would also be a living nightmare for organisations. Think about it: accumulating AI-scale data sets is a long process that you can't afford to do all over again. Worse still, backing it up to be on the safe side is much too costly and may also take a very long time. Leaving it alone is also most definitely not an option as component failure is part and parcel of large-scale systems. Thus, your storage system must have its own protective measures in place. In particular, erasure coding is commonplace in AI storage platforms to ensure more resilient data storage.
Cloud is also another important consideration. Public cloud integration is an important aspect of any data endeavour today. More specifically, cloud-integrated, on-premise storage systems enable flexibility in a more cloud-driven landscape.
However, most important of all is understanding that storage systems are not one-size-fits-all. With everyone exploring AI to serve different initiatives, organisations must do their research and find the solution right for them.
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