If Machine Learning Is the Question, Open Source Is the Answer


In the early days of Linux, for example, the director of IBM’s Linux Technology Center told me that for open source to be successful, you had to have a sufficient body of developers with aptitude and interest in a given area. Every developer needs an operating system, for example, so there tends to be a large body of developers with interest and aptitude in contributing to something like Linux. Ditto databases, app servers (remember them?), and so on.

More recently, Apcera chief executive (and Cloud Foundry architect) Derek Collison told me: “Open source is a natural progression for ecosystems where there’s a lot of innovation and breakthroughs. The market eventually becomes democratized and open source alternatives emerge.” …

Early on, Google made its intentions clear when it open sourced TensorFlow. “We hope this will let the machine learning community – everyone from academic researchers, to engineers, to hobbyists – exchange ideas much more quickly, through working code rather than just research papers,” Google said. With TensorFlow and other open source ML/AI code in mind, Google breeds familiarity with ML and then encourages developers to run their projects on Google Cloud.

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