diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 000000000..0b1ea546b
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,26 @@
+# Use the latest 2.1 version of CircleCI pipeline process engine.
+# See: https://circleci.com/docs/2.0/configuration-reference
+version: 2.1
+
+# Define a job to be invoked later in a workflow.
+# See: https://circleci.com/docs/2.0/configuration-reference/#jobs
+jobs:
+ red-wine-quality:
+ # Specify the execution environment. You can specify an image from Dockerhub or use one of our Convenience Images from CircleCI's Developer Hub.
+ # See: https://circleci.com/docs/2.0/configuration-reference/#docker-machine-macos-windows-executor
+ docker:
+ - image: levkk/postgresml:latest
+ # Add steps to the job
+ # See: https://circleci.com/docs/2.0/configuration-reference/#steps
+ 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"
+
+# Invoke jobs via workflows
+# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
+workflows:
+ end-to-end-tests:
+ jobs:
+ - red-wine-quality
diff --git a/README.md b/README.md
index 3d66a6a4e..9fa79e5fc 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
+[](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.
## Why
diff --git a/sql/install.sql b/sql/install.sql
index c846dcd47..cbef23935 100644
--- a/sql/install.sql
+++ b/sql/install.sql
@@ -6,7 +6,7 @@ CREATE EXTENSION IF NOT EXISTS plpython3u;
---
--- Create schema for models.
---
-DROP SCHEMA pgml CASCADE;
+DROP SCHEMA IF EXISTS pgml CASCADE;
CREATE SCHEMA IF NOT EXISTS pgml;
CREATE OR REPLACE FUNCTION pgml.auto_updated_at(tbl regclass)
diff --git a/sql/test.sql b/sql/test.sql
index 822c0955b..9883ffa85 100644
--- a/sql/test.sql
+++ b/sql/test.sql
@@ -8,12 +8,12 @@ SELECT pgml.version();
\timing
-SELECT pgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
+SELECT * FROM pgml.train('Red Wine Scores', 'regression', 'wine_quality_red', 'quality');
SELECT pgml.predict('Red Wine Scores', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{6.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{5.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
SELECT pgml.predict('Red Wine Scores', '{3.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
-SELECT pgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
+SELECT * FROM pgml.train('Red Wine Categories', 'classification', 'wine_quality_red', 'quality', 'svm');
SELECT pgml.predict('Red Wine Categories', '{7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.99, 2, 0.5, 9.4}');
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