How cognitive tech can handle sensitive data in private clouds | #IBMML

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Big data and analytics have dominated the technology landscape for years. Now IBM Corp. is bringing transactions and machine learning together to extract data’s real value. By leveraging core Watson technologies, IBM will offer the enterprise machine learning capabilities for data that is on a private cloud.

“We are telling clients that you can get the power of machine learning across any type of data, whether its data in a warehouse, a database, unstructured content, email, you name it — we are bringing machine learning everywhere,” said Rob Thomas (pictured), general manager of IBM Analytics at IBM.

Thomas kicked off today’s coverage of the IBM Machine Learning Launch Event on theCUBE, SiliconANGLE Media’s mobile live streaming studio,  with co-hosts Dave Vellante (@dvellante) and Stu Miniman (@stu) by discussing an important announcement made moments earlier.

Machine learning for the enterprise

According to Thomas, 90 percent of the data in the world is sitting behind corporate firewalls because organizations are not ready to upload their sensitive data onto a public cloud. IBM has extracted the machine learning from Watson to enable machine learning to private clouds.

The offering begins with data on the mainframe. Starting with the transactional data that runs businesses such as retailers and banks insurance companies, IBM hopes to show its customers using its z Systems mainframes the power of machine learning and how it exposes the value of their data. The end goal is to extend this capability across data sources, wherever they reside.

The technology allows users to manage all models from a single pane of glass by applying an IBM Research technology called Cognitive Assistance for Data Science, which chooses algorithms for the user, increasing productivity for the data scientist and opening up data to other parts of the organization, such as the business analyst, according to Thomas.

Apache Spark has been fueling the analytics engines across the industry, and IBM has a significant investment in the open-source analytics technology. “This offering is the first world-class applications of Spark. What we are bringing with IBM machine learning is leveraging Spark as an execution engine on the mainframe,” stated Thomas.

When discussing the future of machine learning technology, Thomas would like to see it embedded into all applications, making it easier for all to benefit from the technology. “You need to divide and conquer the machine learning problem where the data scientist can play, the business analyst can play, the app developers can play, the data engineers can play, and that’s what we’re enabling,” concluded Thomas.

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