6.1.1 MySQL HeatWave AutoML Ease of Use

MySQL HeatWave AutoML is purpose-built for ease of use. It requires no machine learning expertise, specialized tools, or algorithms. With MySQL HeatWave AutoML and a set of training data in a MySQL HeatWave DB system, 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. Learn more about Oracle AutoML.

You can use a model created by ML_TRAIN with other MySQL HeatWave AutoML routines to generate predictions and explanations. For example, the following 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', NULL);

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

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

To start using MySQL HeatWave AutoML with sample datasets, see Machine Learning Use Cases.

What's Next

• Learn more about the following:

  • MySQL HeatWave AutoML Learning Types

  • MySQL HeatWave AutoML Workflow

  • Oracle AutoML

• Learn how to Create a Machine Learning Model.