3.1.2 HeatWave AutoML Ease of Use

HeatWave AutoML is purpose-built for ease of use. It requires no machine learning expertise, specialized tools, or algorithms. With HeatWave AutoML and a set of training data, you can train a predictive machine learning model with a single call to the ML_TRAIN SQL routine; for example:

CALL sys.ML_TRAIN('heatwaveml_bench.census_train', 'revenue', NULL, @census_model);

The ML_TRAIN routine leverages Oracle AutoML technology to automate training of machine learning models. For information about Oracle AutoML, see Section 3.1.4, “Oracle AutoML”.

You can use a model created by ML_TRAIN with other HeatWave AutoML routines to generate predictions and explanations; for example, this call to the ML_PREDICT_TABLE routine generates predictions for a table of input data:

CALL sys.ML_PREDICT_TABLE('heatwaveml_bench.census_test', @census_model, 
'heatwaveml_bench.census_predictions');

All HeatWave AutoML operations are initiated by running CALL or SELECT statements, which can be easily integrated into your applications. HeatWave AutoML routines reside in the MySQL sys schema and can be run from any MySQL client or application that is connected to a MySQL DB System with a HeatWave Cluster. HeatWave AutoML routines include:

• ML_TRAIN: Trains a machine learning model for a given training dataset.

• ML_PREDICT_ROW: Makes predictions for one or more rows of data.

• ML_PREDICT_TABLE: Makes predictions for a table of data.

• ML_EXPLAIN_ROW: Explains predictions for one or more rows of data.

• ML_EXPLAIN_TABLE: Explains predictions for a table of data.

• ML_SCORE: Computes the quality of a model.

• ML_MODEL_LOAD: Loads a machine learning model for predictions and explanations.

• ML_MODEL_UNLOAD: Unloads a machine learning model.

In addition, with HeatWave AutoML, there is no need to move or reformat your data. Data and machine learning models never leave the HeatWave Service, which saves you time and effort while keeping your data and models secure.