Stay organized with collectionsSave and categorize content based on your preferences.
End-to-end user journeys for ML models
This document describes the user journeys for machine learning (ML) models that
are trained in BigQuery ML, including the statements and functions that
you can use to work with ML models. BigQuery ML offers the
following types of ML models:
2BigQuery ML doesn't offer a function to retrieve the
weights for this model. To see the weights of the model, you can
export the model from BigQuery ML to Cloud Storage and then use the
XGBoost library or the TensorFlow library to visualize the tree
structure for tree models or the graph structure for neural networks. For more
information, seeEXPORT MODELandExport a BigQuery ML model for online prediction.
Model use user journeys
The following table describes the statements and functions you can use to
evaluate, explain, and get predictions from models:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# End-to-end user journey for each model\n======================================\n\nBigQuery ML supports a variety of machine learning models and a complete machine\nlearning flow for each model, such as feature preprocessing, model creation,\nhyperparameter tuning, inference, evaluation, and model export. The machine\nlearning flow for the models are split into the following two tables:\n\n- [Model creation phase](#model_creation_phase)\n- [Model use phase](#model_use_phase)\n\n\u003cbr /\u003e\n\nModel creation phase\n--------------------\n\n^1^See [TRANSFORM\nclause for the feature engineering](/bigquery/docs/bigqueryml-transform) tutorial. For more information about\nthe preprocessing functions, see the [BQML - Feature Engineering Functions tutorial](https://github.com/GoogleCloudPlatform/bigquery-ml-utils/blob/master/notebooks/bqml-preprocessing-functions.ipynb).\n\n^2^See [use\nhyperparameter tuning to improve model performance](/bigquery/docs/hyperparameter-tuning-tutorial) tutorial.\n\n^3^Automatic feature engineering and hyperparameter tuning are\nembedded in the AutoML model training by default.\n\n^4^The auto.ARIMA algorithm performs hyperparameter tuning for the\ntrend module. Hyperparameter tuning is not supported for the entire modeling\npipeline. See the\n[modeling pipeline](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#time_series_modeling_pipeline) for more details.\n\n^5^BigQuery 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](/bigquery/docs/exporting-models) and the [EXPORT MODEL tutorial](/bigquery/docs/export-model-tutorial).\n\n^6^Uses a\n[Vertex AI foundation model](/vertex-ai/docs/generative-ai/learn/models#foundation_models)\nor customizes it by using supervised tuning.\n\n^7^This is not a typical ML model but rather an artifact that\ntransforms raw data into features.\n\nModel use phase\n---------------\n\n^1^`ml.confusion_matrix` is only applicable to classification models.\n\n^2^`ml.roc_curve` is only applicable to binary classification models.\n\n^3^`ml.explain_predict` is an extended version of `ml.predict`.\nFor more information, see [Explainable AI overview](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-xai-overview).\nTo learn how `ml.explain_predict` is used, see [regression tutorial](/bigquery/docs/linear-regression-tutorial#explain_the_prediction_results) and [classification tutorial](/bigquery/docs/logistic-regression-prediction#explain_the_prediction_results).\n\n^4^For the difference between `ml.global_explain` and\n`ml.feature_importance`, see\n[Explainable AI overview](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-xai-overview).\n\n^5^See the [Export a\nBigQuery ML model for online prediction](/bigquery/docs/export-model-tutorial) tutorial. For more\ninformation about online serving, see the\n[BQML - Create Model with Inline Transpose tutorial](https://github.com/GoogleCloudPlatform/bigquery-ml-utils/blob/master/notebooks/bqml-feature-engineering.ipynb).\n\n^6^For `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.\n\n^7^`ml.explain_forecast` is an extended version of `ml.forecast`.\nFor more information, see [Explainable AI overview](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-xai-overview).\nTo learn how `ml.explain_forecast` is used, see the visualize results steps of the [single time series forecasting](/bigquery/docs/arima-single-time-series-forecasting-tutorial#explain_the_forecasting_results) and [multiple time series forecasting](/bigquery/docs/arima-multiple-time-series-forecasting-tutorial#explain_the_forecasting_results) tutorials.\n\n^8^`ml.advanced_weights` is an extended version of `ml.weights`,\nsee [ml.advanced_weights](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-advanced-weights)\nfor more details.\n\n^9^Uses a\n[Vertex AI foundation model](/vertex-ai/docs/generative-ai/learn/models#foundation_models)\nor customizes it by using supervised tuning.\n\n^10^This is not a typical ML model but rather an artifact that\ntransforms raw data into features.\n\n^11^Not supported for all Vertex AI LLMs. For more information,\nsee\n[ml.evaluate](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate)."]]
