In recent years, thanks to the proliferation of public cloud computing platforms, that’s changing. Companies like Amazon Web Services, Google, Microsoft and IBM have all rolled out cloud-based machine learning platforms. “It’s really lowered the barrier quite a bit,” says Sam Charrington, an analyst and blogger who tracks the machine learning market, adding that the technology is being democratized for everyday developers to use in their applications.
At its most basic level, machine learning is the process of using data to make predictions of future behavior. Most commonly it’s been used in fraud protection (training computers to detect anomalous behavior) and teaching programs to predict future revenues and customer churn. IBM has trained its Watson platform to create sophisticated chatbots for customer interaction and to help healthcare workers provide better care.
It’s still early days for adoption though: A recent study by consultancy Deloitte reported that only 8% of enterprises use machine learening technology today. Allied Market Research predicts the industry is growing at a 33% compound annual growth rate and will reach $13.7 billion by 2020.
“The practice of employing algorithms to parse data, learn from it, and then make a determination … is gathering speed,” reports 451 Researcher Krishna Roy. Consumer adoption of platforms like Amazon’s Echo and Apple’s Siri has seeded this market, but enterprise adoption has been held back by a lack of market education and integration of these systems with existing enterprise platforms. But, she notes that one day this technology could become a “fundamental part of an enterprise's analytics fabric.”
By Brandon Butler
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