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    End-to-end user journey for each model

    BigQuery 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 phase

    Model category
    Model types
    Model creation
    Feature preprocessing
    Hyperparameter tuning
    Model weights
    Feature & training info
    Tutorials
    Supervised learning
    Linear & logistic regression
    create model
    Automatic preprocessing ,
    Manual preprocessing 1
    HP tuning 2
    ml.trial_info
    ml.weights
    ml.feature_info
    ml.training_info
    Deep neural networks (DNN)
    create model
    N/A 5
    N/A
    Wide & Deep networks
    create model
    N/A 5
    N/A
    Boosted trees
    create model
    N/A 5
    N/A
    Random forest
    create model
    N/A 5
    N/A
    AutoML classification & regression
    create model
    N/A 3
    N/A 3
    N/A 5
    N/A
    Unsupervised learning
    K-means
    create model
    Automatic preprocessing ,
    Manual preprocessing 1
    HP tuning 2
    ml.trial_info
    ml.centroids
    ml.feature_info
    ml.training_info
    cluster bike stations
    Matrix factorization
    create model
    N/A
    HP tuning 2
    ml.trial_info
    ml.weights
    Principal component analysis (PCA)
    create model
    Automatic preprocessing ,
    Manual preprocessing 1
    N/A
    ml.principal_
    components     ,
    ml.principal_
    component_info
    N/A
    Autoencoder
    create model
    Automatic preprocessing ,
    Manual preprocessing 1
    HP tuning 2
    ml.trial_info
    N/A 5
    N/A
    Time series models
    ARIMA_PLUS
    create model
    Automatic preprocessing
    auto.ARIMA 4
    ml.arima_ coefficients
    ml.feature_info
    ml.training_info
    ARIMA_PLUS_XREG
    create model
    Automatic preprocessing
    auto.ARIMA 4
    ml.arima_ coefficients
    ml.feature_info
    ml.training_info
    TimesFM
    N/A
    N/A
    N/A
    N/A
    N/A
    forecast multiple time series
    Generative AI remote models
    Remote model over a Vertex AI text generation model 6
    create model
    N/A
    N/A
    N/A
    N/A
    Remote model over a Vertex AI embedding generation model 6
    create model
    N/A
    N/A
    N/A
    N/A
    AI remote models
    Remote model over the Cloud Vision API
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Remote model over the Cloud Translation API
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Remote model over the Cloud Natural Language API
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Remote model over the Document AI API
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Remote model over the Speech-to-Text API
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Remote models
    Remote model with a Vertex AI endpoint
    create model
    N/A
    N/A
    N/A
    N/A
    predict with remote model
    Imported models
    TensorFlow
    create model
    N/A
    N/A
    N/A
    N/A
    predict with imported TensorFlow model
    TensorFlow Lite
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Open Neural Network Exchange (ONNX)
    create model
    N/A
    N/A
    N/A
    N/A
    XGBoost
    create model
    N/A
    N/A
    N/A
    N/A
    N/A
    Transform-only models 7
    Transform-only
    create model
    Manual preprocessing 1
    N/A
    N/A
    ml.feature_info
    N/A
    Contribution analysis models
    Contribution analysis
    create model
    Manual preprocessing
    N/A
    N/A
    N/A
    Get data insights from a contribution analysis model

    1 See TRANSFORM clause for the feature engineering tutorial. For more information about the preprocessing functions, see the BQML - Feature Engineering Functions tutorial .

    2 See use hyperparameter tuning to improve model performance tutorial.

    3 Automatic feature engineering and hyperparameter tuning are embedded in the AutoML model training by default.

    4 The 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.

    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 and the EXPORT MODEL tutorial .

    6 Uses a Vertex AI foundation model or customizes it by using supervised tuning.

    7 This is not a typical ML model but rather an artifact that transforms raw data into features.

    Model use phase

    Model category
    Model types
    Evaluation
    Inference
    AI explanation
    Model monitoring
    Model export
    Tutorials
    Supervised learning
    Linear & logistic regression
    ml.evaluate
    ml.confusion_matrix 1
    ml.roc_curve 2
    ml.predict
    ml.transform
    ml.explain_predict 3
    ml.global_explain
    ml.advanced_weights 8
    ml.describe_data
    ml.validate_data_drift
    ml.validate_data_skew
    ml.tfdv_describe
    ml.tfdv_validate
    export model 5
    Deep neural networks (DNN)
    N/A
    Wide & Deep networks
    N/A
    Boosted trees
    ml.explain_predict 3
    ml.global_explain
    ml.feature_importance 4
    N/A
    Random forest
    N/A
    AutoML classification & regression
    ml.predict
    ml.global_explain
    ml.describe_data
    ml.validate_data_drift
    ml.validate_data_skew
    ml.tfdv_describe
    ml.tfdv_validate
    N/A
    Unsupervised learning
    K-means
    ml.evaluate
    ml.predict
    ml.detect_anomalies
    ml.transform
    N/A
    ml.describe_data
    ml.validate_data_drift
    ml.validate_data_skew
    ml.tfdv_describe
    ml.tfdv_validate
    export model 5
    cluster bike stations
    Matrix factorization
    ml.recommend
    ml.generate_embedding
    N/A
    Principal component analysis (PCA)
    ml.predict
    ml.generate_embedding
    ml.detect_anomalies
    ml.transform
    ml.describe_data
    ml.validate_data_drift
    ml.validate_data_skew
    ml.tfdv_describe
    ml.tfdv_validate
    N/A
    Autoencoder
    ml.predict
    ml.generate_embedding
    ml.detect_anomalies
    ml.reconstruction_loss
    ml.transform
    N/A
    Time series models
    ARIMA_PLUS
    ml.evaluate
    ml.arima_evaluate 6
    ml.holiday_info
    ml.forecast
    ml.detect_anomalies
    ml.explain_forecast 7
    N/A
    N/A
    ARIMA_PLUS_XREG
    ml.forecast
    ml.detect_anomalies
    ml.describe_data
    ml.validate_data_drift
    ml.validate_data_skew
    ml.tfdv_describe
    ml.tfdv_validate
    TimesFM
    N/A
    ai.forecast
    N/A
    N/A
    N/A
    forecast multiple time series
    Generative AI remote models
    Remote model over a Vertex AI text generation model 9
    ml.evaluate 11
    ml.generate_text
    ai.generate_table ( Preview )
    N/A
    N/A
    N/A
    Remote model over a Vertex AI embedding generation model 9
    N/A
    ml.generate_embedding
    N/A
    N/A
    AI remote models
    Remote model over the Cloud Vision API
    N/A
    ml.annotate_image
    N/A
    N/A
    N/A
    N/A
    Remote model over the Cloud Translation API
    N/A
    ml.translate
    N/A
    N/A
    N/A
    Remote model over the Cloud Natural Language API
    N/A
    ml.understand_text
    N/A
    N/A
    N/A
    Remote model over the Document AI API
    N/A
    ml.process_document
    N/A
    N/A
    N/A
    Remote model over the Speech-to-Text API
    N/A
    ml.transcribe
    N/A
    N/A
    N/A
    Remote models
    Remote model with a Vertex AI endpoint
    N/A
    ml.predict
    N/A
    N/A
    N/A
    predict with remote model
    Imported models
    TensorFlow
    N/A
    ml.predict
    N/A
    N/A
    export model 5
    predict with imported TensorFlow model
    TensorFlow Lite
    N/A
    ml.predict
    N/A
    N/A
    N/A
    Open Neural Network Exchange (ONNX)
    N/A
    ml.predict
    N/A
    N/A
    XGBoost
    N/A
    ml.predict
    N/A
    N/A
    N/A
    Transform-only models 10
    Transform-only
    N/A
    ml.transform
    N/A
    N/A
    export model 5
    N/A
    Contribution analysis models
    Contribution analysis
    N/A
    ml.get_insights
    N/A
    N/A
    N/A
    Get data insights from a contribution analysis model

    1 ml.confusion_matrix is only applicable to classification models.

    2 ml.roc_curve is only applicable to binary classification models.

    3 ml.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 .

    4 For the difference between ml.global_explain and ml.feature_importance , see Explainable AI overview .

    5 See 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 .

    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.

    7 ml.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.

    8 ml.advanced_weights is an extended version of ml.weights , see ml.advanced_weights for more details.

    9 Uses a Vertex AI foundation model or customizes it by using supervised tuning.

    10 This is not a typical ML model but rather an artifact that transforms raw data into features.

    11 Not supported for all Vertex AI LLMs. For more information, see ml.evaluate .
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