Model creation

BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.

A typical model development workflow in BigQuery ML looks similar to the following:

  1. Create the model using the CREATE MODEL statement.
  2. Perform feature preprocessing. Some preprocessing happens automatically, plus you can use manual preprocessing functions inside the TRANSFORM clause to do additional preprocessing.
  3. Refine the model by performing hyperparameter tuning to fit the model to the training data.
  4. Evaluate the model to assess how it might perform on data outside of the training set, and also to compare it to other models if appropriate.
  5. Perform inference to analyze data by using the model.
  6. Provide explainability for the model, to clarify how particular features influenced a given prediction and also the model overall.
  7. Learn more about the components that comprize the model by using model weights.

Because you can use many different kinds of models in BigQuery ML, the functions available for each model vary. See the End-to-end user journey for each model to see the specific functions available for each model.