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Preliminaries

  1. Make sure your python environment has mindsdb and ludwig installed.

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MindsDB example commands

We will follow the “home rentals” tutorial available on the cloud learning hub:
  1. Select a subsample of the data to inspect it:
SELECT * 
FROM example_db.demo_data.home_rentals 
LIMIT 10;
  1. Create an AI Table with Ludwig as the ML backend:
CREATE MODEL 
  mindsdb.home_rentals_ludwig_model
FROM example_db
  (SELECT * FROM demo_data.home_rentals)
PREDICT rental_price
USING
engine='ludwig';
  1. Check the status of the predictor. It may take a while to finish training:
SELECT * FROM mindsdb.models where name='home_rentals_ludwig_model';
  1. Make a prediction. Note that for the time being Ludwig requires all input columns to be specified, an error will trigger if data for any of them is missing:
SELECT rental_price
FROM mindsdb.home_rentals_ludwig_model
WHERE sqft = 823
AND number_of_rooms=2
AND number_of_bathrooms=1
AND location='good'
AND neighborhood='downtown'
AND days_on_market=10;