Is Your Storage Architecture Ready for the Coming AI Wave?
- by 7wData
Artificial Intelligence (AI) is a broad term that can apply to various computing tasks, including machine learning, deep learning, and big data analytics. Many AI projects are in a proof of concept stage, but CIOs and IT Managers need to understand that in the future, almost every business outcome and workflow will use and depend upon some form of AI processing. The time is now to prepare the Infrastructure for that eventuality. As AI environments move into production and begin to grow in size and importance, organizations need a strategy to address challenges the AI at scale will create for both the compute and storage architectures.
For the last decade, developing a cloud strategy was at the top of every CIO’s to-do list. Developing an AI strategy will quickly replace developing a cloud strategy, but the AI strategy is more critical. The cloud strategy impacts where the Organization should store and process data, while the AI strategy will, quite literally, impact what business the Organization does and how it does it. It is also reasonable to expect that most organizations will use each of the AI workload types to drive their businesses.
Before organizations can establish an AI strategy, they need to clear up AI misconceptions. A common misconception is that AI is a single type of workload. In reality, each of the sub-types of AI processing (deep learning, machine learning, and big data analytics), are very distinct workloads with unique storage and IO characteristics. Each of these workloads requires different capabilities from the storage architecture. For example, image recognition today is dominated by deep learning techniques, and the workload that deep learning applies to the storage Infrastructure is very different from the workload imposed by the other AI technologies. Organizations need a strategy to select a single storage infrastructure that meets the needs of the various AI workload types.
For most organizations, the journey to an AI infrastructure starts with infrastructures that look very similar to High-Performance computing (HPC) Infrastructures. Some similarities make HPC a logical starting point. HPC and AI both consist of many compute nodes, parallel file systems, and scale-out storage nodes to meet capacity and performance needs.
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