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12/12/2016
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5 Things Security Pros Need To Know About Machine Learning

Experts share best practices for data integrity, pattern recognition and computing power to help enterprises get the most out of machine learning-based technology for cybersecurity.
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#3 Generating A ML Model Can Require Extensive Computing Power

If you are doing anything with Big Data, data sets that are significantly large, then you want to make sure you have the computing power to do the machine learning because it is computationally intensive to train a model to learn from data, Wolff says. Typically, you are not going to be able to do this on your laptop. A lot of times you need clusters of computers to do computing modeling. Cloud service providers offer clusters of servers that you can spin up and down to do things, but several like Microsofts Azure, offer a machine learning library companies can deploy as well.

If you wanted to buy machine learning in your own organization and you have a lot of data, chances are you are going to need a cluster to run it whether it is a CPU or GPU cluster, depending on what type of modeling you want to do, Wolff says.

Cylance trains its technology on a cloud computing platform with extensive computing processing capabilities. The nice thing about these models is once you have trained them, theyve learned, and [when] you want them to tell you something it takes a lot less CPUs to do so, Wolff says. He says Cylance trains its models on 100s of machines in the cloud. Once theyve learned you can put the technology on a laptop and run with a regular CPU.

Image Source: By Scanrail1 via Shutterstock

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JonKim
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JonKim,
User Rank: Author
12/15/2016 | 3:02:27 PM
Insightful
Insightful, thank you for sharing.
gopinathmohan861
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gopinathmohan861,
User Rank: Apprentice
12/14/2016 | 10:11:16 AM
Machine Learning - Useful points
First of all, a big thanks for the article. The informations (5 security pros) mentioned in this article very useful. As AI and ML is going to rule future world, we need to consider these security pros.
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