The CREATE MODEL statement

To create a model in BigQuery, use the BigQuery ML CREATE MODEL statement. This statement is similar to the CREATE TABLE DDL statement. When you run a query that contains a CREATE MODEL statement, a query job is generated for you that processes the query. You can also use the Google Cloud console user interface to create a model by using a UI ( Preview ).

For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model .

Required permissions

• To create a dataset to store the model, you need the bigquery.datasets.create IAM permission.

• To create a model, you need the following permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.connections.delegate (for remote models)

The following predefined IAM roles grant these permissions:

• BigQuery Studio Admin
• BigQuery Admin

For more information about IAM roles and permissions in BigQuery, see Introduction to IAM .

CREATE MODEL syntax

{ CREATE MODEL 
| CREATE MODEL IF NOT EXISTS 
| CREATE OR REPLACE MODEL 
}  model_name 
 
[TRANSFORM (  select_list 
 
)]
[INPUT (  field_name 
 
  field_type 
 
)
 OUTPUT (  field_name 
 
  field_type 
 
)]
[REMOTE WITH CONNECTION {`  connection_name 
 
` | DEFAULT}]
[OPTIONS(  model_option_list 
 
)]
[ AS 
{  query_statement 
 
|
  (
    training_data AS (  query_statement 
 
),
    custom_holiday AS (  holiday_statement 
 
)
  )}] model_option_list: 
 MODEL_TYPE 
= { 'LINEAR_REG' 
| 'LOGISTIC_REG' 
| 'KMEANS' 
| 'MATRIX_FACTORIZATION' 
| 'PCA' 
| 'AUTOENCODER' 
| 'AUTOML_CLASSIFIER' 
| 'AUTOML_REGRESSOR' 
| 'BOOSTED_TREE_CLASSIFIER' 
| 'BOOSTED_TREE_REGRESSOR' 
| 'RANDOM_FOREST_CLASSIFIER' 
| 'RANDOM_FOREST_REGRESSOR' 
| 'DNN_CLASSIFIER' 
| 'DNN_REGRESSOR' 
| 'DNN_LINEAR_COMBINED_CLASSIFIER' 
| 'DNN_LINEAR_COMBINED_REGRESSOR' 
| 'ARIMA_PLUS' 
| 'ARIMA_PLUS_XREG' 
| 'TENSORFLOW' 
| 'TENSORFLOW_LITE' 
| 'ONNX' 
| 'XGBOOST' 
| 'CONTRIBUTION_ANALYSIS' 
}
    [, MODEL_REGISTRY 
= { 'VERTEX_AI' } ]
    [, VERTEX_AI_MODEL_ID 
= string_value 
]
    [, VERTEX_AI_MODEL_VERSION_ALIASES 
= string_array 
]
    [, INPUT_LABEL_COLS 
= string_array 
]
    [, MAX_ITERATIONS 
= int64_value 
]
    [, EARLY_STOP 
= { TRUE | FALSE } ]
    [, MIN_REL_PROGRESS 
= float64_value 
]
    [, DATA_SPLIT_METHOD 
= { 'AUTO_SPLIT' | 'RANDOM' | 'CUSTOM' | 'SEQ' | 'NO_SPLIT' } ]
    [, DATA_SPLIT_EVAL_FRACTION 
= float64_value 
]
    [, DATA_SPLIT_TEST_FRACTION 
= float64_value 
]
    [, DATA_SPLIT_COL 
= string_value 
]
    [, OPTIMIZE_STRATEGY 
= { 'AUTO_STRATEGY' | 'BATCH_GRADIENT_DESCENT' | 'NORMAL_EQUATION' } ]
    [, L1_REG 
= float64_value 
]
    [, L2_REG 
= float64_value 
]
    [, LEARN_RATE_STRATEGY 
= { 'LINE_SEARCH' | 'CONSTANT' } ]
    [, LEARN_RATE 
= float64_value 
]
    [, LS_INIT_LEARN_RATE 
= float64_value 
]
    [, WARM_START 
= { TRUE | FALSE } ]
    [, AUTO_CLASS_WEIGHTS 
= { TRUE | FALSE } ]
    [, CLASS_WEIGHTS 
= struct_array 
]
    [, INSTANCE_WEIGHT_COL 
= string_value 
]
    [, NUM_CLUSTERS 
= int64_value 
]
    [, KMEANS_INIT_METHOD 
= { 'RANDOM' | 'KMEANS++' | 'CUSTOM' } ]
    [, KMEANS_INIT_COL 
= string_value 
]
    [, DISTANCE_TYPE 
= { 'EUCLIDEAN' | 'COSINE' } ]
    [, STANDARDIZE_FEATURES 
= { TRUE | FALSE } ]
    [, MODEL_PATH 
= string_value 
]
    [, BUDGET_HOURS 
= float64_value 
]
    [, OPTIMIZATION_OBJECTIVE 
= { string_value 
| struct_value 
} ]
    [, FEEDBACK_TYPE 
= {'EXPLICIT' | 'IMPLICIT'} ]
    [, NUM_FACTORS 
= int64_value 
]
    [, USER_COL 
= string_value 
]
    [, ITEM_COL 
= string_value 
]
    [, RATING_COL 
= string_value 
]
    [, WALS_ALPHA 
= float64_value 
]
    [, BOOSTER_TYPE 
= { 'gbtree' | 'dart'} ]
    [, NUM_PARALLEL_TREE 
= int64_value 
]
    [, DART_NORMALIZE_TYPE 
= { 'tree' | 'forest'} ]
    [, TREE_METHOD 
= { 'auto' | 'exact' | 'approx' | 'hist'} ]
    [, MIN_TREE_CHILD_WEIGHT 
= float64_value 
]
    [, COLSAMPLE_BYTREE 
= float64_value 
]
    [, COLSAMPLE_BYLEVEL 
= float64_value 
]
    [, COLSAMPLE_BYNODE 
= float64_value 
]
    [, MIN_SPLIT_LOSS 
= float64_value 
]
    [, MAX_TREE_DEPTH 
= int64_value 
]
    [, SUBSAMPLE 
= float64_value 
]
    [, ACTIVATION_FN 
= { 'RELU' | 'RELU6' | 'CRELU' | 'ELU' | 'SELU' | 'SIGMOID' | 'TANH' } ]
    [, BATCH_SIZE 
= int64_value 
]
    [, DROPOUT 
= float64_value 
]
    [, HIDDEN_UNITS 
= int_array 
]
    [, OPTIMIZER 
= { 'ADAGRAD' | 'ADAM' | 'FTRL' | 'RMSPROP' | 'SGD' } ]
    [, TIME_SERIES_TIMESTAMP_COL 
= string_value 
]
    [, TIME_SERIES_DATA_COL 
= string_value 
]
    [, TIME_SERIES_ID_COL 
= { string_value 
| string_array 
} ]
    [, HORIZON 
= int64_value 
]
    [, AUTO_ARIMA 
= { TRUE | FALSE } ]
    [, AUTO_ARIMA_MAX_ORDER 
= int64_value 
]
    [, AUTO_ARIMA_MIN_ORDER 
= int64_value 
]
    [, NON_SEASONAL_ORDER 
= (int64_value, int64_value, int64_value) 
]
    [, DATA_FREQUENCY 
= { 'AUTO_FREQUENCY' | 'PER_MINUTE' | 'HOURLY' | 'DAILY' | 'WEEKLY' | ... } ]
    [, FORECAST_LIMIT_LOWER_BOUND 
= float64_value 
]
    [, FORECAST_LIMIT_UPPER_BOUND 
= float64_value 
]
    [, INCLUDE_DRIFT 
= { TRUE | FALSE } ]
    [, HOLIDAY_REGION 
= { 'GLOBAL' | 'NA' | 'JAPAC' | 'EMEA' | 'LAC' | 'AE' | ... } ]
    [, CLEAN_SPIKES_AND_DIPS 
= { TRUE | FALSE } ]
    [, ADJUST_STEP_CHANGES 
= { TRUE | FALSE } ]
    [, DECOMPOSE_TIME_SERIES 
= { TRUE | FALSE } ]
    [, HIERARCHICAL_TIME_SERIES_COLS 
= { string_array 
} ]
    [, ENABLE_GLOBAL_EXPLAIN 
= { TRUE | FALSE } ]
    [, APPROX_GLOBAL_FEATURE_CONTRIB 
= { TRUE | FALSE }]
    [, INTEGRATED_GRADIENTS_NUM_STEPS 
= int64_value 
]
    [, CALCULATE_P_VALUES 
= { TRUE | FALSE } ]
    [, FIT_INTERCEPT 
= { TRUE | FALSE } ]
    [, CATEGORY_ENCODING_METHOD 
= { 'ONE_HOT_ENCODING' | 'DUMMY_ENCODING' | 'LABEL_ENCODING' | 'TARGET_ENCODING' } ]
    [, ENDPOINT 
= string_value 
]
    [, REMOTE_SERVICE_TYPE 
= { 'CLOUD_AI_VISION_V1' | 'CLOUD_AI_NATURAL_LANGUAGE_V1' | 'CLOUD_AI_TRANSLATE_V3' } ]
    [, XGBOOST_VERSION 
= { '0.9' | '1.1' } ]
    [, TF_VERSION 
= { '1.15' | '2.8.0' } ]
    [, NUM_TRIALS 
= int64_value 
, ]
    [, MAX_PARALLEL_TRIALS 
= int64_value 
]
    [, HPARAM_TUNING_ALGORITHM 
= { 'VIZIER_DEFAULT' | 'RANDOM_SEARCH' | 'GRID_SEARCH' } ]
    [, HPARAM_TUNING_OBJECTIVES 
= { 'R2_SCORE' | 'ROC_AUC' | ... } ]
    [, NUM_PRINCIPAL_COMPONENTS 
= int64_value 
]
    [, PCA_EXPLAINED_VARIANCE_RATIO 
= float64_value 
]
    [, SCALE_FEATURES 
= { TRUE | FALSE } ]
    [, PCA_SOLVER 
= { 'FULL' | 'RANDOMIZED' | 'AUTO' } ]
    [, TIME_SERIES_LENGTH_FRACTION 
= float64_value 
]
    [, MIN_TIME_SERIES_LENGTH 
= int64_value 
]
    [, MAX_TIME_SERIES_LENGTH 
= int64_value 
]
    [, TREND_SMOOTHING_WINDOW_SIZE 
= int64_value 
]
    [, SEASONALITIES 
= string_array 
]
    [, PROMPT_COL 
= string_value 
]
    [, LEARNING_RATE_MULTIPLIER 
= float64_value 
]
    [, ACCELERATOR_TYPE 
= { 'GPU' | 'TPU' } ]
    [, EVALUATION_TASK 
= { 'TEXT_GENERATION' | 'CLASSIFICATION' | 'SUMMARIZATION' | 'QUESTION_ANSWERING' | 'UNSPECIFIED' } ]
    [, DOCUMENT_PROCESSOR 
= string_value 
]
    [, SPEECH_RECOGNIZER 
= string_value 
]
    [, KMS_KEY_NAME 
= string_value 
]
    [, CONTRIBUTION_METRIC 
= string_value 
]
    [, DIMENSION_ID_COLS 
= string_array 
]
    [, IS_TEST_COL 
= string_value 
]
    [, MIN_APRIORI_SUPPORT 
= float64_value 
]
    [, PRUNING_METHOD 
= {'NO_PRUNING', 'PRUNE_REDUNDANT_INSIGHTS'} 
]
    [, TOP_K_INSIGHTS_BY_APRIORI_SUPPORT 
= int64_value 
]

CREATE MODEL

Creates and trains a new model in the specified dataset. If the model name exists, CREATE MODEL returns an error.

CREATE MODEL IF NOT EXISTS

Creates and trains a new model only if the model does not exist in the specified dataset.

CREATE OR REPLACE MODEL

Creates and trains a model and replaces an existing model with the same name in the specified dataset.

model_name

model_name is the name of the model you're creating or replacing. The model name must be unique per dataset: no other model or table can have the same name. The model name must follow the same naming rules as a BigQuery table. A model name can:

• Contain up to 1,024 characters
• Contain letters (upper or lower case), numbers, and underscores

model_name is not case-sensitive.

If you don't have a default project configured, prepend the project ID to the model name in following format, including backticks: `[PROJECT_ID].[DATASET].[MODEL]` ; for example, `myproject.mydataset.mymodel` .

TRANSFORM

TRANSFORM lets you specify all preprocessing during model creation and have it automatically applied during prediction and evaluation.

For example, you can create the following model:

  CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 mymodel 
 ` 
  
 TRANSFORM 
 ( 
 ML 
 . 
 FEATURE_CROSS 
 ( 
 STRUCT 
 ( 
 f1 
 , 
  
 f2 
 )) 
  
 as 
  
 cross_f 
 , 
  
 ML 
 . 
 QUANTILE_BUCKETIZE 
 ( 
 f3 
 ) 
  
 OVER 
 () 
  
 as 
  
 buckets 
 , 
  
 label_col 
 ) 
  
 OPTIONS 
 ( 
 model_type 
 = 
 'linear_reg' 
 , 
  
 input_label_cols 
 = 
 [ 
 'label_col' 
 ]) 
 AS 
  
 SELECT 
  
 * 
  
 FROM 
  
 t 
 

During prediction, you don't need to preprocess the input again, and the same transformations are automatically restored:

  SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 PREDICT 
 ( 
 MODEL 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 mymodel 
 ` 
 , 
  
 ( 
 SELECT 
  
 f1 
 , 
  
 f2 
 , 
  
 f3 
  
 FROM 
  
 table 
 )) 
 

When the TRANSFORM clause is present, only output columns from the TRANSFORM clause are used in training. Any results from query_statement that don't appear in the TRANSFORM clause are ignored.

The input columns of the TRANSFORM clause are the result of query_statement . So, the final input used in training is the set of columns generated by the following query:

  SELECT 
  
 ( 
 select_list 
 ) 
  
 FROM 
  
 ( 
 query_statement 
 ); 
 

Input columns of the TRANSFORM clause can be of any SIMPLE type or ARRAY of SIMPLE type. SIMPLE types are non-STRUCT and non-ARRAY data types.

In prediction ( ML.PREDICT ), users only need to pass in the original columns from the query_statement that are used inside the TRANSFORM clause. The columns dropped in TRANSFORM don't need to be provided during prediction. TRANSFORM is automatically applied to the input data during prediction, including the statistics used in ML analytic functions (for example, ML.QUANTILE_BUCKETIZE ).

To learn more about feature preprocessing, see Feature preprocessing overview , or try the Feature Engineering Functions notebook.

To try using the TRANSFORM clause, try the Use the BigQuery ML TRANSFORM clause for feature engineering tutorial or the Create Model With Inline Transpose notebook.

select_list

You can pass columns from query_statement through to model training without transformation by either using * ,  * EXCEPT() , or by listing the column names directly.

Not all columns from query_statement are required to appear in the TRANSFORM clause, so you can drop columns appearing in query_statement by omitting them from the TRANSFORM clause.

You can transform inputs from query_statement by using expressions in select_list . select_list is similar to a normal SELECT statement. select_list supports the following syntax:

• *
• * EXCEPT()
• * REPLACE()
• expression
• expression.*

The following cannot appear inside select_list :

• Aggregation functions.
• Non-BigQuery ML analytic functions. For more information about supported functions, see Manual feature preprocessing .
• UDFs.
• Subqueries.
• Anonymous columns. For example, a + b as c is allowed, while a + b isn't.

The output columns of select_list can be of any BigQuery supported data type.

If present, the following columns must appear in select_list without transformation:

• label
• data_split_col
• kmeans_init_col
• instance_weight_col

If these columns are returned by query_statement , you must reference them in select_list by column name outside of any expression, or by using * . You can't use aliases with these columns.

INPUT and OUTPUT

INPUT and OUTPUT clauses are used to specify input and output format for remote models or XGBoost models .

field_name

For remote models, INPUT and OUTPUT field names must be identical as the field names of the Vertex AI endpoint request and response. See examples in remote model INPUT and OUTPUT clause .

For XGBoost models, INPUT field names must be identical to the names in the feature_names field if feature_names field is populated in the XGBoost model file. See XGBoost INPUT OUTPUT clause for more details.

field_type

Remote models support the following BigQuery data types for INPUT and OUTPUT clauses:

• Simple type: BOOL , INT64 , FLOAT64 , NUMERIC , BIGNUMERIC , STRING
• ARRAY <Simple type>

XGBoost models support the following BigQuery data types for INPUT field type:

• Numeric type
• ARRAY <Numeric type>

XGBoost models only support FLOAT64 for OUTPUT field type.

connection_name

BigQuery uses a CLOUD_RESOURCE connection to interact with your Vertex AI endpoint. You need to grant Vertex AI User role to connection's service account on your Vertex AI endpoint project.

See examples in remote model CONNECTION statement .

To use a default connection , specify specify DEFAULT instead of the connection name.

model_option_list

CREATE MODEL supports the following options:

MODEL_TYPE

Syntax

  MODEL_TYPE 
  
 = 
  
 { 
  
 'LINEAR_REG' 
  
 | 
  
 'LOGISTIC_REG' 
  
 | 
  
 'KMEANS' 
  
 | 
  
 'PCA' 
  
 | 
 'MATRIX_FACTORIZATION' 
  
 | 
  
 'AUTOENCODER' 
  
 | 
  
 'AUTOML_REGRESSOR' 
  
 | 
 'AUTOML_CLASSIFIER' 
  
 | 
  
 'BOOSTED_TREE_CLASSIFIER' 
  
 | 
  
 'BOOSTED_TREE_REGRESSOR' 
  
 | 
 'RANDOM_FOREST_CLASSIFIER' 
  
 | 
  
 'RANDOM_FOREST_REGRESSOR' 
  
 | 
 'DNN_CLASSIFIER' 
  
 | 
  
 'DNN_REGRESSOR' 
  
 | 
  
 'DNN_LINEAR_COMBINED_CLASSIFIER' 
  
 | 
 'DNN_LINEAR_COMBINED_REGRESSOR' 
  
 | 
  
 'ARIMA_PLUS' 
  
 | 
  
 'ARIMA_PLUS_XREG' 
  
 | 
 'TENSORFLOW' 
  
 | 
  
 'TENSORFLOW_LITE' 
  
 | 
  
 'ONNX' 
  
 | 
  
 'XGBOOST' 
  
 | 
  
 'CONTRIBUTION_ANALYSIS' 
 } 
 

Description

Specify the model type. This argument is required.

Arguments

The argument is in the model type column.

Other model options

The following table provides a comprehensive list of model options, with a brief descriptions and their applicable model types. You can find detailed description in the model specific CREATE MODEL statement by clicking the model type in the "Applied model types" column.

When the applied model types are supervised learning models, unless "regressor" or "classifier" is explicitly listed, it means that model options apply to both the regressor and the classifier. For example, the "boosted tree" means that model option applies to both boosted tree regressor and boosted tree classifier, while the "boosted tree classifier" only applies to the classifier.

AS

AS  query_statement 
 

For time series forecasting models that have a DATA_FREQUENCY value of either DAILY or AUTO_FREQUENCY , you can optionally use the following AS clause syntax to perform custom holiday modeling in addition to specifying the training data:

AS (
  training_data AS (  query_statement 
 
),
  custom_holiday AS (  holiday_statement 
 
)
)

query_statement

The query_statement argument specifies the query that is used to generate the training data. For information about the supported SQL syntax of the query_statement clause, see GoogleSQL query syntax .

holiday_statement

The holiday_statement argument specifies the query that provides custom holiday modeling information for time series forecast models. This query must return 50,000 rows or less and must contain the following columns:

• region : Required. A STRING value that identifies the region to target for holiday modeling. Use one of the following options:

  • An upper-case holiday region code . Use this option to overwrite or supplement the holidays for the specified region. You can see the holidays for a region by running SELECT * FROM bigquery-public-data.ml_datasets.holidays_and_events_for_forecasting WHERE region = region .
  • An arbitrary string. Use this option to specify a custom region that you want to model holidays for. For example, you could specify London if you are only modeling holidays for that city.

  Be sure not to use an existing holiday region code when you are trying to model for a custom region. For example, if you want to model a holiday in California, and specify CA as the region value, the service recognizes that as the holiday region code for Canada and targets that region. Because the argument is case-sensitive, you could specify ca , California , or some other value that isn't a holiday region code.

• holiday_name : Required. A STRING value that identifies the holiday to target for holiday modeling. Use one of the following options:

  • The holiday name as it is represented in the bigquery-public-data.ml_datasets.holidays_and_events_for_forecasting public table, including case. Use this option to overwrite or supplement the specified holiday.
  • A string that represents a custom holiday. The string must be a valid column name so that it can be used in ML.EXPLAIN_FORECAST output. For example, it cannot contain space. For more information on column naming, see Column names .

• primary_date : Required. A DATE value that specifies the date the holiday falls on.

• preholiday_days : Optional. An INT64 value that specifies the start of the holiday window around the holiday that is taken into account when modeling. Must be greater than or equal to 1 . Defaults to 1 .

• postholiday_days : Optional. An INT64 value that specifies the end of the holiday window around the holiday that is taken into account when modeling. Must be greater than or equal to 1 . Defaults to 1 .

The preholiday_days and postholiday_days arguments together describe the holiday window around the holiday that is taken into account when modeling. The holiday window is defined as [primary_date - preholiday_days, primary_date + postholiday_days] and is inclusive of the pre- and post-holiday days. The value for each holiday window must be less than or equal to 30 and must be the same across the given holiday. For example, if you are modeling Arbor Day for several different years, you must specify the same holiday window for all of those years.

To achieve the best holiday modeling result, provide as much historical and forecast information about the occurrences of each included holiday as possible. For example, if you have time series data from 2018 to 2022 and would like to forecast for 2023, you get the best result by providing the custom holiday information for all of those years, similar to the following:

 CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 `mydataset.arima_model` 
  
 OPTIONS 
  
 ( 
  
 model_type 
  
 = 
  
 'ARIMA_PLUS' 
 , 
  
 holiday_region 
  
 = 
  
 'US' 
 ,...) 
  
 AS 
  
 ( 
  
 training_data 
  
 AS 
  
 ( 
 SELECT 
  
 * 
  
 FROM 
  
 `mydataset.timeseries_data` 
 ), 
  
 custom_holiday 
  
 AS 
  
 ( 
  
 SELECT 
  
 'US' 
  
 AS 
  
 region 
 , 
  
 'Halloween' 
  
 AS 
  
 holiday_name 
 , 
  
 primary_date 
 , 
  
 5 
  
 AS 
  
 preholiday_days 
 , 
  
 1 
  
 AS 
  
 postholiday_days 
  
 FROM 
  
 UNNEST 
 ( 
  
 [ 
  
 DATE 
 ( 
 '2018-10-31' 
 ), 
  
 DATE 
 ( 
 '2019-10-31' 
 ), 
  
 DATE 
 ( 
 '2020-10-31' 
 ), 
  
 DATE 
 ( 
 '2021-10-31' 
 ), 
  
 DATE 
 ( 
 '2022-10-31' 
 ), 
  
 DATE 
 ( 
 '2023-10-31' 
 ) 
 ] 
 ) 
  
 AS 
  
 primary_date 
  
 ) 
  
 ) 

Supported inputs

The CREATE MODEL statement supports the following data types for input label, data split columns and input feature columns.

Supported input feature types

See Supported input feature types for BigQuery ML supported input feature types.

Supported data types for input label columns

BigQuery ML supports different GoogleSQL data types depending on the model type. Supported data types for input_label_cols include:

Supported data types for data split columns

BigQuery ML supports different GoogleSQL data types depending on the data split method. Supported data types for data_split_col include:

Limitations

CREATE MODEL statements must comply with the following rules:

• Only one CREATE statement is allowed.
• When you use a CREATE MODEL statement, the size of the model must be 90 MB or less or the query fails. Generally, if all categorical variables are short strings, a total feature cardinality (model dimension) of 5-10 million is supported. The dimensionality is dependent on the cardinality and length of the string variables.
• The label column cannot contain NULL values. If the label column contains NULL values, then the query fails.
• The CREATE MODEL IF NOT EXISTS clause always updates the last modified timestamp of a model.
• Query statements used in the CREATE MODEL statement cannot contain EXTERNAL_QUERY . If you want to use EXTERNAL_QUERY , then materialize the query result and then use the CREATE MODEL statement with the newly created table.