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What If We Could Use Machine Learning Models as Tables – Percona Live ONLINE Talk Preview

 | October 12, 2020 |  Posted In: MariaDB, PLO-2020-10, PostgreSQL

Percona Live Online Agenda Slot: Tue 20 Oct • New York 1:30 p.m. • London 6:30 p.m. • New Delhi 11:00 p.m. • Singapore 1:30 a.m. (next day)


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.

Why is your talk exciting?

ML straight in databases is exciting because it enables hundreds of thousands of people that already know SQL to solve problems using machine learning without any extra skills.

Who would benefit the most from your talk?

Anyone knows how to query a SQL database.

Is there any other question you would like to answer?

What databases can we do machine learning in now, and which ones are coming?

What other talks are you most looking forward to?

Jorge Torres
Jorge Torres

Jorge Torres is the Co-founder & CEO of MindsDB. He is also a visiting scholar at UC Berkeley researching machine learning automation and explainability. Prior to founding MindsDB, he worked for a number of data-intensive start-ups, most recently working with Aneesh Chopra (the first CTO in the US government) building data systems that analyze billions of patients records and lead to highest savings for millions of patients. He started his work on scaling solutions using machine learning in early 2008 while working as first full time engineer at Couchsurfing where he helped grow the company from a few thousand users to a few million. Jorge had degrees in electrical engineering & computer science, including a masters degree in computer systems (with a focus on applied Machine Learning) from the Australian National University.

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