Machine learning and artificial intelligence are enjoying rapidly increasing adoption, and understandably so; these technologies promise tremendous business advantages and advances across all industries. A report from Enterprise Strategy Group found that 64% of IT decision-makers surveyed expected AI/ML spending to increase in 2021.
To operate, these technologies use huge pools of unstructured data – often in the form of things like videos, images, text and voice. Workloads of these types require a new approach to data storage; the old ways won’t suffice. With the advent of such workloads, applications need faster access to massive amounts of data – data that is created everywhere: in the cloud, at the edges and on-premises. These intensive workloads require low latency, the ability to support different types and sizes of payloads, and the ability to scale linearly.
What’s needed is a fresh approach to data delivery, one that is application-centric rather than location- or technology-centric.
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The widespread adoption of AI/ML and analytics means enterprise IT leaders need a significant shift in the way they think about data management and storage.
Support for Mixed Workloads for AI/ML World
When it comes to data storage and AI/ML workloads, what’s needed is a solution that can handle different types of workloads, both small and large files. In some cases, you may need to deal with just a few tens of terabytes, while in others, there are many petabytes.
Not all solutions are meant for huge files, just as not all can handle very small ones. The trick is finding one that can handle both in a flexible manner.
The Need for Scale
Algorithms for AI/ML need enormous datasets to allow for proper training of underlying models that ensure accuracy and speed. Organizations want to grow in terms of capacity and performance but are often hampered by traditional storage solutions. When they try to scale linearly, they are unable to. AI/ML workloads require a storage solution that can scale infinitely as the data grows.
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While legacy file and block storage solutions are unable to scale after a few hundred terabytes, object storage can scale limitlessly, elastically and seamlessly based on demand. And what’s important about object storage compared with traditional storage is that it’s a completely flat space in which there are no limitations. Users won’t encounter the limitations they’d find with traditional storage.
The Performance Factor
It isn’t enough to scale in terms of capacity – it is also important to scale linearly in terms of performance. Unfortunately, with many traditional storage solutions, scaling capacity comes at the expense of performance. So, when an organization needs to scale linearly in terms of capacity, performance tends to plateau or decline.
Traditional storage is based on files organized into a hierarchy, with directories and sub-directories. This architecture works quite well for a small capacity of data, but as capacity grows, performance suffers beyond a certain capacity due to system bottlenecks and limitations with file lookup tables.
However, object storage provides an unlimited flat namespace so that by simply adding additional nodes, you can scale to petabytes and beyond. For this reason, you can scale for performance as you scale for capacity.
A Storage Solution for Our Times
As AI and ML gain momentum, enterprises must find a new approach to storage that enables them to properly set up, run and scale their AI/ML initiatives. Some of today’s enterprise-grade object storage software is purpose-built for the demands of AI/ML training. Organizations can start their projects on a small scale, on one server, and easily scale out both capacity and performance as needed. Fast object storage brings essential performance to the analytics applications these initiatives need, too. What’s more, it can enable flexibility from the edge to the core and offer complete data lifecycle management across multiple Clouds. Object storage lets applications easily access data on-premises, even in multiple clouds, for efficient data processing. For the low latency, the ability to support different types and sizes of payloads and the ability to scale linearly that enterprise AI/ML projects need, fast object storage fills the bill.
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