Machine learning in your database

Postgres Innovation

Lev Kokotov and Montana Low explore the innovative use of PostgreSQL for machine learning applications. Lev shares how they developed PG Cat, a tool that simplifies sharding and load balancing, making PostgreSQL highly available without the need for complex configurations 1. Montana discusses the early stages of PostgresML, an extension aimed at integrating machine learning capabilities directly into PostgreSQL, which they hope will streamline data processing and model deployment 2.

The simplicity that you can get from having a single datastore instead of every database technology in the world... will lead you to a much better place in the end.

--- Montana Low

Their goal is to create a seamless experience for users by combining these technologies into an online service.

   

Efficiency Gains

The discussion highlights PostgreSQL's role in enhancing operational efficiency within data systems. Montana Low emphasizes the practicality of using PostgreSQL for data science tasks, noting that most business applications don't require complex deep learning models 3. Instead, PostgreSQL allows for efficient data manipulation and feature engineering directly within the database, reducing the need for extensive data movement 4.

People always focus on the math behind the algorithms... but what they don't focus on... is the curation of the data.

--- Montana Low

This approach minimizes latency and complexity, making PostgreSQL a powerful tool for data-driven decision-making.