In most machine learning tasks, one has to first organize data in some form and then turn it into information about the problem that needs to be solved.
One could say that the requirement to train many machine learning algorithms is information, not just data. Given that most of the world's structured and semi-structured data (information) lives in databases, it makes sense to bring ML capabilities straight to the databases themselves. In this talk we want to present to the Percona community what we have learned in the effort of enabling existing databases like MariaDB and Postgres with frictionless ML powers.
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