version:2.1

jobs:

red-wine-quality:

docker:

-image:levkk/postgresml:latest

steps:

-checkout

-run:

name:"Train and predict"

command:"bash docker/entrypoint.sh psql -U root -p 5432 -h 127.0.0.1 -f sql/test.sql"

workflows:

end-to-end-tests:

jobs:

-red-wine-quality


[](https://circleci.com/gh/postgresml/postgresml/tree/master)

PostgresML is an end-to-end machine learning system. Using only SQL, it allows to train models and run online predictions, alongside normal queries, directly using the data in your databases.

DROPSCHEMA pgml CASCADE;

DROPSCHEMAIF EXISTSpgml CASCADE;

CREATESCHEMAIF NOT EXISTS pgml;

CREATE OR REPLACEFUNCTIONpgml.auto_updated_at(tbl regclass)

\timing

SELECTpgml.train('Red Wine Scores','regression','wine_quality_red','quality');

SELECT*FROMpgml.train('Red Wine Scores','regression','wine_quality_red','quality');

SELECTpgml.predict('Red Wine Scores','{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

SELECTpgml.predict('Red Wine Scores','{6.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

SELECTpgml.predict('Red Wine Scores','{5.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

SELECTpgml.predict('Red Wine Scores','{3.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');

SELECTpgml.train('Red Wine Categories','classification','wine_quality_red','quality','svm');

SELECT*FROMpgml.train('Red Wine Categories','classification','wine_quality_red','quality','svm');

SELECTpgml.predict('Red Wine Categories','{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');