SQL

Machine learning models can take input data directly from a SQL database like PostgreSQL.

Some reasons for that:

• SQL databases are commonly used for storing large volumes of structured data that models can be trained and scored on. Integrating with SQL allows models to leverage existing data infrastructure.

• Models can query only the new/updated rows as needed rather than processing all the data each time.

• SQL databases allow joining model input features from multiple related tables as needed. This can help models make better predictions by incorporating more context.

• Storing predictions back in SQL allows those results to then be queried, analyzed, and accessed by other downstream applications and processes.

• Analytics engine for large-scale data processing, like Apache Spark, have a SQL API.