The key to AI success: a data-centric architecture

The fourth industrial revolution, led by artificial intelligence (AI) and machine learning, will transform our society and this revolution has begun.

Admittedly, AI is not a new technology. It has existed since the 1950s, but until recently was reserved for academic projects and a number of major companies in the world. To date, everything has become accessible, thanks to the alliance of three key technologies: deep learning software, graphics processing units (GPUs) and Big Data.

Create AI platform

Inspired by the human brain, deep learning utilizes giant parallel neural networks and creates its own software by feeding a significant amount of information. The GPU, on the other hand, makes it possible to implement complex algorithms that are similar to human brains. These two revolutionary technologies have allowed great breakthroughs. And when the third part of the puzzle, Big Data, in addition, we have an incredible potential for innovation. However, when the first two rapid advances, the traditional Big Data architecture based on traditional storage technologies has become a real barrier to innovation. This creates a gap in performance between computational factors (deep learning and GPU) and storage, limiting the opportunities for companies to leverage their exponential growth data.

Unlock the potential of data by redeveloping the infrastructure

The development of AI is based on immediate and rapid data processing, but in a traditional architecture, moving and replicating data means significant cost and congestion. The exponential growth of data requires a rethink of traditional methods to put data into the center of the infrastructure and avoid moving them from the old to the new system. Therefore, the computational factors are put directly into the data instead of the opposite. It saves time and money and helps companies focus on data mining and value creation.

The system must be able to operate in real time, support next-generation analytics, be available on demand and self-managed (to avoid the need for continuous management), allowing IT to play a role. Host a hosting service provider for the rest of the business. Consolidation and simplification of the architecture using flash technology will allow IT teams to focus on AI technology.

As storage can exploit Big Data, GPU and deep learning

By optimizing the combination of computation and storage, organizations can set up a benchmark architecture for deployment. This provides the GPU with an ideal storage infrastructure that combines the speed of a local storage system with the simplicity, benefits and unified shared storage.

Companies like Paige.AI have used this optimization method to calculate and store to support their AI projects. Paige.AI is an organization that has revolutionized the clinical diagnosis of cancer and treated with AI. Pathology is the basis of most cancer diagnoses. However, most pathologic diagnoses are based on manual and subjective procedures developed over a century ago. By harnessing the potential of AI, Paige.AI aims to transform pathology and diagnostics into a rigorous discipline based on quantitative rather than qualitative ones.

By accessing one of the world’s largest tumor repositories, the company requires the most advanced learning infrastructure to quickly convert large amounts of data into AI applications. clinically confirmed.

With AI and deep learning, analysis capabilities reach higher levels in all areas. By 2020, Gartner predicts the prevalence of AI in most of the software products and services available. For this to become a reality, companies must ensure that data is at the center of their IT strategy. If they do not adopt a data-centric architecture, companies can still try to harness the computing power of in-depth learning and the GPU, but the results will be less. The true success of AI depends on the perfect combination of computing power and storage. Without this, the data would not reveal its full potential.

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