A majority of developers involved with machine learning do not use models to generate predictions. Even among those that work with training data sets directly or via an API, only 10 percent work with more than a million rows. Technologists focusing on real-time, Big Data analytics better narrow their focus a bit. Those are just two findings based on SlashData’s State of the Developer Nation 15th Edition, which asked detailed questions of over 4,200 developers involved with machine learning (ML) and data science beyond just using someone else’s algorithms, building frameworks, or artificial intelligence (AI) not related to ML.
When asked if their team uses models to generate predictions, 58 percent said no, that at most they use models to describe data. Twenty-eight percent use batch processing for predictions while 21 percent make real-time predictions. The batch and real-time data may be slightly higher because the question didn’t allow for multiple responses, but we still believe that this level of adoption represents proof that there is demand for stream processing to analyze data continuously and quickly
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