diff --git a/README.md b/README.md
index 9e6a5c1a2..6d0caf828 100644
--- a/README.md
+++ b/README.md
@@ -195,69 +195,9 @@ Colocating the compute with the data inside the database removes one of the most
-### Installation in WSL or Ubuntu
+### Development
-Install Python3, pip, and Pl/Python3:
-
-```bash
-sudo apt update
-sudo apt install -y postgresql-plpython3-12 python3 python3-pip
-```
-
-Restart the Postgres server:
-
-```bash
-sudo service postgresql restart
-```
-
-Create the extension:
-
-```sql
-CREATE EXTENSION plpython3u;
-```
-
-Install Scikit globally (I didn't bother setup Postgres with a virtualenv, but it's possible):
-
-```
-sudo pip3 install sklearn
-```
-
-### Run the example
-
-```bash
-psql -f scikit_train_and_predict.sql
-```
-
-Example output:
-
-```
-psql:scikit_train_and_predict.sql:4: NOTICE: drop cascades to view scikit_train_view
-DROP TABLE
-CREATE TABLE
-psql:scikit_train_and_predict.sql:14: NOTICE: view "scikit_train_view" does not exist, skipping
-DROP VIEW
-CREATE VIEW
-INSERT 0 500
-CREATE FUNCTION
- scikit_learn_train_example
-----------------------------
- OK
-(1 row)
-
-CREATE FUNCTION
- value | weight | prediction
--------+--------+------------
- 1 | 5 | 5
- 2 | 5 | 5
- 3 | 5 | 5
- 4 | 5 | 5
- 5 | 5 | 5
-(5 rows)
-```
-
-### Run the linear model
-
-Install our PgML package globally:
+Follow the installation instructions to create a local working Postgres environment, then install your PgML package from the git repository:
```
cd pgml
diff --git a/scikit_import.sql b/scikit_import.sql
deleted file mode 100644
index 1afcc6e66..000000000
--- a/scikit_import.sql
+++ /dev/null
@@ -1,10 +0,0 @@
-CREATE EXTENSION IF NOT EXISTS plpython3u;
-
-CREATE OR REPLACE FUNCTION pgml_version()
-RETURNS TEXT
-AS $$
- import pgml
- return pgml.version()
-$$ LANGUAGE plpython3u;
-
-SELECT pgml_version();
diff --git a/scikit_train_and_predict.sql b/scikit_train_and_predict.sql
deleted file mode 100644
index 6f8b5c990..000000000
--- a/scikit_train_and_predict.sql
+++ /dev/null
@@ -1,71 +0,0 @@
---
--- CREATE EXTENSION
---
-CREATE EXTENSION IF NOT EXISTS plpython3u;
-
---
--- Data table.
---
-DROP TABLE IF EXISTS scikit_train_data CASCADE;
-CREATE TABLE scikit_train_data (
- id BIGSERIAL PRIMARY KEY,
- value BIGINT,
- weight DOUBLE PRECISION
-);
-
---
--- View of the data table, just to demonstrate that views work.
---
-DROP VIEW IF EXISTS scikit_train_view;
-CREATE VIEW scikit_train_view AS SELECT * FROM scikit_train_data;
-
---
--- Insert some dummy data into the data table.
---
-INSERT INTO scikit_train_data (value, weight) SELECT generate_series(1, 500), 5.0;
-
-
-CREATE OR REPLACE FUNCTION scikit_learn_train_example()
-RETURNS BYTEA
-AS $$
- from sklearn.ensemble import RandomForestClassifier
- import pickle
-
- cursor = plpy.cursor("SELECT value, weight FROM scikit_train_view")
- X = []
- y = []
-
- while True:
- rows = cursor.fetch(5)
- if not rows:
- break
- for row in rows:
- X.append([row["value"],])
- y.append(row["weight"])
- rfc = RandomForestClassifier()
- rfc.fit(X, y)
-
- return pickle.dumps(rfc)
-
-$$ LANGUAGE plpython3u;
-
-;
-
-CREATE OR REPLACE FUNCTION scikit_learn_predict_example(model BYTEA, value INT)
-RETURNS DOUBLE PRECISION
-AS $$
- import pickle
-
- m = pickle.loads(model)
-
- r = m.predict([[value,]])
- return r[0]
-$$ LANGUAGE plpython3u;
-
-WITH model as (
- SELECT scikit_learn_train_example() AS pickle
-)
-SELECT value,
- weight,
- scikit_learn_predict_example((SELECT model.pickle FROM model), value::int) AS prediction
-FROM scikit_train_view LIMIT 5;
diff --git a/benchmarks.sql b/sql/benchmarks.sql
similarity index 100%
rename from benchmarks.sql
rename to sql/benchmarks.sql
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