Google BigQuery's Machine Learning functionality allows you to create, train, and use Machine Learning models directly within BigQuery, using SQL-like queries.

Below is an introductory guide on how you can work with ML models in BigQuery:

Create ML Models #

Creating a model involves writing a SQL query that includes the CREATE MODEL statement. An example of such a statement is:

CREATE OR REPLACE MODEL `your_project.your_dataset.your_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
label,
feature1,
feature2
FROM
`your_project.your_dataset.your_training_data`

The CREATE MODEL statement creates and trains the model using the specified training data and options. The OPTIONS clause is used to specify the model options, such as the model type.

Evaluate ML Models #

After creating a model, you can evaluate its performance using the ML.EVALUATE function. An example query to evaluate a model is:

SELECT
*
FROM
ML.EVALUATE(MODEL `your_project.your_dataset.your_model`,
(SELECT
label,
feature1,
feature2
FROM
`your_project.your_dataset.your_evaluation_data`))

This function provides several evaluation metrics specific to the model type.

Use ML Models for Prediction #

You can use your model to predict outcomes for new data using the ML.PREDICT function. An example query to make predictions using a model is:

SELECT
predicted_label,
feature1,
feature2
FROM
ML.PREDICT(MODEL `your_project.your_dataset.your_model`,
(SELECT
feature1,
feature2
FROM
`your_project.your_dataset.your_prediction_data`))

This function outputs a table with the original input data and an additional column for each predicted label.

Manage ML Models #

You can manage your models using the SHOW MODELS statement to list all models in a dataset, the DROP MODEL statement to delete a model, and the GET MODEL statement to get information about a model.

For example, to list all models in a dataset:

SHOW MODELS `your_project.your_dataset`

To delete a model:

DROP MODEL `your_project.your_dataset.your_model`

And to get information about a model:

SELECT
*
FROM
ML.MODEL_INFO(MODEL `your_project.your_dataset.your_model`)

Remember to replace your_project, your_dataset, your_model, your_training_data, your_evaluation_data, and your_prediction_data with your actual project, dataset, model, and data identifiers.