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Machine Learning and Developer Programs

September 29, 2016

It looks like machine learning has taken the developer world by storm.  Among those developers who are working on projects that involve Big Data today, over a third (35%) are using some form of machine learning in their applications.  And while the finance and insurance industry shares the top spot on the list of targeted industries for machine learning with IoT, they each only account for 13.4% of the implementations.  This shows a highly fragmented market, where no one industry dominates in the types of applications that are being imagined and created by developers for machine learning solutions.


In response to the huge interest in this form of artificial intelligence, major manufacturers are racing with each other to provide tools and APIs to facilitate ML on their platforms.  IBM has long been offering Watson APIs on their Blue Mix platform, while Microsoft has an entire Cortana development suite on Azure.  Amazon provides ML APIs for AWS. HP has Haven on Demand.  The list goes on and on.


But other than supplying the tools and APIs for developers to use, how else can ML benefit developer programs?


It turns out that there is actually a very rich set of capabilities that can be added to a developer program through implementing machine learning.  Just a few thoughts come to mind.  How about using ML to sort developer inquiries in an intelligent way to spot common themes or problems with products or tools?  Or, maybe when a developer accesses an API, use ML to suggest other appropriate APIs or tools?  ML can be used to track a developer’s interests and movements within the developer program portal and then anticipate his needs and offer suggestions for additional documentation, training or tools based on his past behavior.  And, of course, chatbots can be used to supplement tech support and training – maybe even one day replace the need for humans in those functions.


When we measured the complementary technologies being used, real time event processing was cited as a factor in 30.8% of ML applications. Image recognition and description (the ability to spot faces or specific things) was a factor in 28.9% of organizations’ use cases, and pattern recognition (the ability to see the same thing again) accounted for 28.3%. Video processing was cited in better than one use case in four (25.6%), suggesting strong applications for surveillance and physical security.


Those are use cases for the general population, but with just a touch of imagination, these can all be folded into a developer program to provide new and exciting offerings to support developers and enhance their experience with your program.


One Comment leave one →
  1. December 3, 2018 2:59 am

    Hi, Thanks for Sharing nice blog

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