Information = AI

AI = Actionable Insights

For companies to make decisions, data must become information. Until recently, processing the vast quantities of data has been very difficult if not impossible. In the last few years, converging hardware and software technologies make the impossible, possible.

Artificial intelligence and big data analytics technologies are key technologies in the world of data science. The challenges are significant. Data science requires specialized expertise. Giving data scientists tools they need to become and stay efficient will make your projects more successful.

GPU computing is critical to AI, data science, and HPC. It is no surprise that a data science workstation is built with massive GPU computing power. Of course raw power is not enough for AI and  data science. Every AI developer and data scientist needs a high performance software stack. An optimized, GPU accelerated software stack is what sets a data science workstation apart from other powerful workstations.

For your AI and data science projects to succeed, you need the right tools. This means parallel computing power and productive software. The data science workstation from NVIDIA is designed to deliver both.

Artificial intelligence and big data analytics technologies are key technologies in the world of data science. The challenges are significant. Data science requires specialized expertise. Giving data scientists tools they need to become and stay efficient will make your projects more successful.

GPU computing is critical to AI, data science, and HPC. It is no surprise that a data science workstation is built with massive GPU computing power. Of course raw power is not enough for AI and  data science. Every AI developer and data scientist needs a high performance software stack. An optimized, GPU accelerated software stack is what sets a data science workstation apart.

For your AI and data science projects to succeed, you need the right tools. This means parallel computing power and productive software. The data science workstation from NVIDIA is designed to deliver both.

Why you need a data science workstation

Download the white paper, "Why you need a data science workstation" and read how a data science workstation  makes your project more efficient.

The GPU is a powerful processor and an inherently parallel-processing device. It is burdened with performing complex mathematical operations for real-time 3D graphics and generating a continuous flow of millions of pixels. For real-time rendering on a typical 4K display, a GPU will produce between half a billion and one billion pixels per second.

It makes sense that a powerful, highly parallel computing engine like the GPU would be a perfect match for the parallel processing needs required by neural network training. Two advantages are particularly interesting for AI. The GPU is a high-performance processor that is well-adapted to AI calculations. And GPUs are mass produced and therefore widely available and affordable.

This combination allows GPUs to train the larger neural networks needed for deep learning with massive data sets. The increase in pure parallel processing performance reduced training times by 50%, 70%, and more. Training that would have required months or even a year were reduced to a week or even days.

More on AI

Why Is The Artificial Intelligence Market Booming?

GPU accelerated data science & AI

Today, Artificial Intelligence is synonymous with neural networks. For decades, this was not the case. The widespread development and adoption of neural network techniques has led to a boom in AI. But why? And why now?

 

A Data Science Workstation with Experts Pre-installed

TensorFlow on the Scan 3XS Data Science Workstation

Information is everything. Turning mountains of data into valuable information requires a workstation fully configured with exactly the right hardware and software. Scan has the answer for you.

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