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How NVIDIA Could Dominate Machine Learning

Most major technology companies are knee-deep in machine learning these days. Alphabet's (NASDAQ: GOOG) (NASDAQ: GOOGL) Google, Amazon, and Facebook (NASDAQ: FB) are just a few. Machine learning allows the tech companies' computers to learn information on their own that they weren't programmed to know.

For example, Google uses its own TensorFlow machine learning systems for its Google Translate speech recognition app, Google Photos, Gmail, and its Web searches.  

And as these companies dive further into machine learning, they're building their own complex computers using graphics processing units (GPUs) to power them -- and that could be particularly beneficial for NVIDIA (NASDAQ: NVDA).

The company makes some of the most popular GPUs for gaming, but the hardware is increasingly finding its way into supercomputers. 

Facebook already uses NVIDIA's Tesla M40 GPU accelerators to help power its Big Sur machine learning computers. These NVIDIA GPUs were specifically designed to train deep neural networks for enterprise data centers, and the company says they're 10 to 20 times faster than other neural network computers.

NVIDIA says its GPU-powered machine learning computers can help train neural networks to learn new things in just a few hours, as opposed to days or weeks with less powerful systems. 

What's the real potential here?

OK, so many of the world's best technology companies are already using NVIDIA's GPU-powered machine learning systems, but how much could NVIDIA make from this?

According to MarketsandMarkets, the cognitive computing market (which includes natural language learning, machine learning, and automated reasoning) is expected to be worth $12 billion by 2019.

And NVIDIA's already positioned itself to benefit from the enterprise machine learning market. Back in April, the company released its new Tesla P100 GPU for corporate data centers.

The company said these chips pack 15 billion transistors on them, which is about twice as many as Intel's recently debuted server processors.

That's important because technology companies are increasingly looking to advanced cloud computing systems. The global cloud computing market is worth $204 billion right now, and NVIDIA is already tapping into this market by supplying cloud computing GPUs to Microsoft, Google, and Amazon.

Foolish final thoughts

NVIDIA's bread and butter is still its gaming revenue, which comprised $687 million of the company's $1.3 billion total revenue in fiscal Q1 2017. Meanwhile, its data center revenue, which includes its GPU sales for cloud-based and machine learning services brought in just $143 million in the quarter.

But it's worth remembering that the company has recently started focusing on segments outside of gaming, and its making huge moves in a short amount of time. For example, the company's data center revenue increased by 63% year over year in the last quarter. 

Investors looking for NVIDIA machine learning growth should keep a close watch on the company's datacenter revenue, particularly next year when the Tesla P100 starts finding its way into more servers. 

I don't expect NVIDIA's revenue to spike from GPU sales for cloud-based machine learning, but rather steadily increase as the company builds out its data center segment. With the company's growing list of machine learning GPU customers, NVIDIA is poised to benefit from machine learning's growth (and its thirst for more complex data processing). But investors are going to have to wait at least a few quarters to find out how well these machine learning investments have paid off.

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