Machine learning is the new SQL

This is a guest blogpost by Oleg Rogynskyy, VP of Marketing & Growth, H2O.ai Before the emergence of today’s massive data sets, organisations primarily stored their data in relational databases produced by the likes of Oracle, Teradata, IBM, etc. Following its emergence in the latter half of the 1980s, SQL quickly became the de facto standard for working with those databases. While there are differences between various vendor flavors of SQL, the language itself follows the same general pattern, allowing business analysts without a developer background to quickly pick it up and leverage the insights from the data stored in their relational databases. Today, I think machine learning is democratising the big data era of Hadoop and Sparkin much the same way that SQL did for relational databases. The problem…


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