Machine learning beyond data scientists: the self-serve model emerges | #IBMML

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Machine learning has recently come into its own because of the potential for applied automation in finding patterns and correlations in data sets. It’s “very much at the heart of modern applications that developers build,” said James Kobielus (pictured), senior program director of product marketing and big data analytics, IBM Analytics, at IBM Corp.

While machine learning has been around for a number of years in the form of artificial neural networks, recent developments have commercialized and refined the tools of machine learning to a much greater degree, making them far more useful to data scientists. Today’s launch for IBM‘s Machine Learning platform, delivered first to IBM’s z System mainframes, will greatly help data scientists automate and process the data that they need, Kobielus explained.

Kobielus joined Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s mobile live streaming studio, during the IBM Machine Learning Launch Event in New York, NY, to discuss the potential of machine learning that extends beyond data scientists. (*Disclosure below.)

The future of machine learning

In the past 10 years, Kobielus has seen the business-intelligence community move toward self-service and predictive analytics. He visualizes a similar dynamic coming into machine learning, with the democratization of data built on a self-service model. The day will come when users can build machine learning and deep learning models without the need for a university education, he stated. It is just a matter of the maturation of technology in the marketplace over time.

Kobielus explained that it is useful to think of the computer age as consisting for three eras. The first was in the years before WWII, where there was an emphasis on electromechanical computing devices. The second era, extending from WWII to the present day, was focused around software. At the turn of 2010, we have entered the third era of cognitive computing, where computers are learning the business rules automatically from the data itself. This involves machine learning at its very heart, he concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the IBM Machine Learning Launch Event 2017 NYC. (*Disclosure: TheCUBE is a media partner at the conference. Neither IBM nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo by SiliconANGLE