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Top five ways to bring machine learning into your project


By Alex Handy


 Alex Handy
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May 9, 2012 —  (Page 1 of 2)
By now, every software developer can list off at least two majorly dangerous artificial intelligence projects from the fantasy world of Hollywood. While HAL and Skynet are renowned for using their digital intelligences to further their nefarious plans for murder, in reality, AI is a lot less dangerous. For the most part, AI in the modern application is all about improving application performance over time, not about usurping a space ship or starting a nuclear war.

Here, then, are five ways to bring AI into your application. We promise that none of these suggestions will result in humanity being wiped out.

Mahout
Mahout is so named for the fellow who tends the elephant. It's an Indian word, and it’s fairly apt to the goals of the project. However, right now, Mahout is still in its infancy: The current release is version 0.6. But Mahout is likely to become the most compelling machine-learning choice over the long term because it is built on top of Hadoop.

And Hadoop, as we all know, allows you to store humongous amounts of data for analysis. Of course, machine learning is all about having a big data set to learn from, and you really can't get much bigger than Hadoop is capable of hosting.

Given a few petabytes of information, a Mahout application could crunch down its algorithms and hone them to a razor’s edge. For now, those algorithms will best be used to do similarity assessment; users who liked this might also like that. But as Mahout matures and begins to include more-complex algorithms, there's no reason why enterprises won't be able to optimize their Hadoop data on the fly via a Mahout application.

BigML
While the Hadoop folks have all the fun of running servers, installing packages, writing MapReduce jobs, and generally behaving like IT ops folks, BigML has created an alternative to on-premise AI. BigML is, essentially, machine learning as a service. You upload your data, then begin constructing AI on top of it that can learn from that data set.

That means developers just have to stop by bigml.com/developers, generate an API key, and in a few minutes, they're able to work with BigML via its RESTful API.

The overall goal at BigML is to make machine learning easy and beautiful. We can safely say that because it has removed the hardware from the equation, and it's certainly made things a lot easier already.


Related Search Term(s): AI, BigML, machine learning, Mahout, Opera, PyBrain, Stanford

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