MindsDB Announces integrations With Open Source Relational Databases PostgreSQL And MySQL

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  • MindsDB brings machine learning to those who work with data to allow users to create and deploy ML models using standard SQL queries and increase AI projects’ efficiency
  • With the newly announced MindsDB integrations, MySQL and PostgreSQL users can use AI-Tables to learn and make predictions from their data with no machine learning experiences

MindsDB, the open source AI layer for existing databases has announced official integrations with open source relational databases PostgreSQL and MySQL. They said that these join a growing list of integrations with community-driven databases including MariaDB and Clickhouse to bring the machine learning capabilities of MindsDB to over 55 per cent of open source databases.

MindsDB brings machine learning to those who work with data to allow users to create and deploy ML models using standard SQL queries and increase AI projects’ efficiency. By using AI-tables, database users can apply machine learning models straight from their database and automatically generate predictions as simple as querying a table.

30 years of active community development

PostgreSQL is an open source object-relational database system with over 30 years of active community development. The database has a strong reputation for reliability, feature robustness, and performance. MySQL, owned by Oracle is trusted by organisations such as Facebook, Google, and Adobe. Together, the two represent 45 per cent of the active open source database market.

MindsDB co-founder, Adam Carrigan said, “Bringing machine learning resources to the open source database community is a huge part of our mission to democratize machine learning. Staying connected to this community has helped us identify the main challenges of users that know their data best and give them machine learning tools to help them solve those problems.”

With the newly announced MindsDB integrations, MySQL and PostgreSQL users can use AI-Tables to learn and make predictions from their data with no machine learning experiences. Users can now run a simple SQL query to deploy automated machine learning models directly inside their database.

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