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End-to-end user journey for each modelBigQuery ML supports a variety of machine learning models and a complete machine learning flow for each model, such as feature preprocessing, model creation, hyperparameter tuning, inference, evaluation, and model export. The machine learning flow for the models are split into the following two tables:
Model creation phase1See TRANSFORM clause for the feature engineering tutorial. For more information about the preprocessing functions, see the BQML - Feature Engineering Functions tutorial.
2See use hyperparameter tuning to improve model performance tutorial.
3Automatic feature engineering and hyperparameter tuning are embedded in the AutoML model training by default.
4The auto.ARIMA algorithm performs hyperparameter tuning for the trend module. Hyperparameter tuning is not supported for the entire modeling pipeline. See the modeling pipeline for more details.
5BigQuery ML doesn't support functions that retrieve the weights for boosted trees, random forest, DNNs, Wide-and-deep, Autoencoder, or AutoML models. To see the weights of those models, you can export an existing model from BigQuery ML to Cloud Storage and then use the XGBoost library or the TensorFlow library to visualize the tree structure for the tree models or the graph structure for the neural networks. For more information, see the EXPORT MODEL documentation and the EXPORT MODEL tutorial.
6Uses a Vertex AI foundation model or customizes it by using supervised tuning.
7This is not a typical ML model but rather an artifact that transforms raw data into features.
Model use phase1ml.confusion_matrix is only applicable to classification models.
2ml.roc_curve is only applicable to binary classification models.
3ml.explain_predict is an extended version of ml.predict. For more information, see Explainable AI overview. To learn how ml.explain_predict is used, see regression tutorial and classification tutorial.
4For the difference between ml.global_explain and ml.feature_importance, see Explainable AI overview.
5See the Export a BigQuery ML model for online prediction tutorial. For more information about online serving, see the BQML - Create Model with Inline Transpose tutorial.
6For ARIMA_PLUS or ARIMA_PLUS_XREG models, ml.evaluate can take new data as input to compute forecasting metrics such as mean absolute percentage error (MAPE). In the absence of new data, ml.evaluate has an extended version ml.arima_evaluate which outputs different evaluation information.
7ml.explain_forecast is an extended version of ml.forecast. For more information, see Explainable AI overview. To learn how ml.explain_forecast is used, see the visualize results steps of the single time series forecasting and multiple time series forecasting tutorials.
8ml.advanced_weights is an extended version of ml.weights, see ml.advanced_weights for more details.
9Uses a Vertex AI foundation model or customizes it by using supervised tuning.
10This is not a typical ML model but rather an artifact that transforms raw data into features.
11Not supported for all Vertex AI LLMs. For more information, see ml.evaluate.
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Last updated 2025-08-07 UTC.
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