Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production.

Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA.Easy? Not even close, says Ted Dunning, chief application architect at MapR, but “anybody who thinks that they can just buy magic bullets off the shelf has no business” buying machine learning technology in the first place.[ Learn how to write apps that take full advantage of machine learning: Data in, intelligence out: Machine learning pipelines demystified • Google’s machine-learning cloud pipeline explained • R and Python drive SQL Server 2017 into machine learning. | Keep up with hot topics in programming with InfoWorld’s App Dev Report newsletter. ]
“Unless you already know about machine learning and how to bring it to production, you probably don’t understand the complexities that you are about to add to your companies life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator,” Dunning says.To read this article in full or to leave a comment, please click here

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