'Human-friendly project name',

'regression',

'<name of the table or view containing the data>',

);

SELECTpgml.predict('Human-friendly project name', ARRAY[...])AS prediction_score;

where`ARRAY[...]` is a list ofthefeatures for which we want to run a prediction. This list has to be in the same order as the columns in the data table. This score then can be used in normal queries, for example:

where`ARRAY[...]` is a list of features for which we want to run a prediction. This list has to be in the same order as the columns in the data table. This score then can be used in normal queries, for example:

SELECT*,

This project is currently a proof of concept. Some important features which we are currently thinking about or working on are listed below.

This project is currently a proof of concept. Some important features, which we are currently thinking about or working on, are listed below.

A data explorer allows anyone to browse the dataset in production and to find useful tables and features to build effective machine learning models.

Scikit-Learn is a good start, but we're also thinking about including Tensorflow, Pytorch, and many more useful models.

In applications where data changes often, it's useful to retrain the models automatically on a schedule, e.g. every day, every week, etc.

*How far can this scale?*