Google Research

Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires

IEEE Symposium on Visual Language and Human-Centric Computing (VL/HCC) (2019)

Abstract

The growing popularity of machine learning (ML) has attracted more software developers to now want to adopt ML into their own practices, through tinkering with and learning from ML framework websites and online code examples. To investigate the motivations, hurdles, and desires of these software developers, we deployed a survey to the website of the TensorFlow.js ML framework. We found via 645 responses that many wanted to learn ML for aspirational reasons rather than for immediate job needs. Critically, developers faced hurdles due to a perceived lack of mathematical and theoretical background. They desired frameworks to provide more basic ML conceptual support, such as a curated corpus of best practices, conceptual tutorials, and a de-mystification of mathematical jargon into practical tips. These findings inform the design of ML frameworks and informal learning resources to broaden the base of people acquiring this increasingly important skill set.

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