diff --git a/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.py b/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.py
new file mode 100644
index 0000000..fb26f13
--- /dev/null
+++ b/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.py
@@ -0,0 +1,232 @@
+from typing import NamedTuple
+
+from kfp.components import create_component_from_func
+
+def train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML(
+    # AutoMLTabularTrainingJob.run required parameters
+    dataset_name: 'GoogleCloudVertexAiTabularDatasetName',
+    target_column: str,
+
+    # AutoMLTabularTrainingJob.__init__ required parameters
+    # display_name: str,
+    optimization_prediction_type: str,
+
+    # AutoMLTabularTrainingJob.run parameters
+    training_fraction_split: float = 0.8,
+    validation_fraction_split: float = 0.1,
+    test_fraction_split: float = 0.1,
+    predefined_split_column_name: str = None,
+    weight_column: str = None,
+    budget_milli_node_hours: int = 1000,
+    model_display_name: str = None,
+    disable_early_stopping: bool = False,
+
+    # AutoMLTabularTrainingJob.__init__ parameters
+    optimization_objective: str = None,
+    #column_transformations: Union[Dict, List[Dict], NoneType] = None,
+    optimization_objective_recall_value: float = None,
+    optimization_objective_precision_value: float = None,
+
+    project: str = None,
+    location: str = 'us-central1',
+    #training_encryption_spec_key_name: str = None,
+    #model_encryption_spec_key_name: str = None,
+    encryption_spec_key_name: str = None,
+) -> NamedTuple('Outputs', [
+    ('model_name', 'GoogleCloudVertexAiModelName'),
+    ('model_dict', dict),
+]):
+    '''Trains model using Google Cloud Vertex AI AutoML.
+
+    Data fraction splits:
+    Any of ``training_fraction_split``, ``validation_fraction_split`` and
+    ``test_fraction_split`` may optionally be provided, they must sum to up to 1. If
+    the provided ones sum to less than 1, the remainder is assigned to sets as
+    decided by Vertex AI. If none of the fractions are set, by default roughly 80%
+    of data will be used for training, 10% for validation, and 10% for test.
+
+    Annotations:
+        author: Alexey Volkov <alexey.volkov@ark-kun.com>
+
+    Args:
+        dataset_name:
+            Required. The full name of dataset  (datasets.TabularDataset) within the same Project from which data will be used to train the Model. The
+            Dataset must use schema compatible with Model being trained,
+            and what is compatible should be described in the used
+            TrainingPipeline's [training_task_definition]
+            [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
+            For tabular Datasets, all their data is exported to
+            training, to pick and choose from.
+        target_column (str):
+            Required. The name of the column values of which the Model is to predict.
+        training_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to train the Model. This is ignored if Dataset is not provided.
+        validation_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to validate the Model. This is ignored if Dataset is not provided.
+        test_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to evaluate the Model. This is ignored if Dataset is not provided.
+        predefined_split_column_name (str):
+            Optional. The key is a name of one of the Dataset's data
+            columns. The value of the key (either the label's value or
+            value in the column) must be one of {``training``,
+            ``validation``, ``test``}, and it defines to which set the
+            given piece of data is assigned. If for a piece of data the
+            key is not present or has an invalid value, that piece is
+            ignored by the pipeline.
+
+            Supported only for tabular and time series Datasets.
+        weight_column (str):
+            Optional. Name of the column that should be used as the weight column.
+            Higher values in this column give more importance to the row
+            during Model training. The column must have numeric values between 0 and
+            10000 inclusively, and 0 value means that the row is ignored.
+            If the weight column field is not set, then all rows are assumed to have
+            equal weight of 1.
+        budget_milli_node_hours (int):
+            Optional. The train budget of creating this Model, expressed in milli node
+            hours i.e. 1,000 value in this field means 1 node hour.
+            The training cost of the model will not exceed this budget. The final
+            cost will be attempted to be close to the budget, though may end up
+            being (even) noticeably smaller - at the backend's discretion. This
+            especially may happen when further model training ceases to provide
+            any improvements.
+            If the budget is set to a value known to be insufficient to train a
+            Model for the given training set, the training won't be attempted and
+            will error.
+            The minimum value is 1000 and the maximum is 72000.
+        model_display_name (str):
+            Optional. If the script produces a managed Vertex AI Model. The display name of
+            the Model. The name can be up to 128 characters long and can be consist
+            of any UTF-8 characters.
+
+            If not provided upon creation, the job's display_name is used.
+        disable_early_stopping (bool):
+            Required. If true, the entire budget is used. This disables the early stopping
+            feature. By default, the early stopping feature is enabled, which means
+            that training might stop before the entire training budget has been
+            used, if further training does no longer brings significant improvement
+            to the model.
+
+        optimization_prediction_type (str):
+            The type of prediction the Model is to produce.
+            "classification" - Predict one out of multiple target values is
+            picked for each row.
+            "regression" - Predict a value based on its relation to other values.
+            This type is available only to columns that contain
+            semantically numeric values, i.e. integers or floating
+            point number, even if stored as e.g. strings.
+        optimization_objective (str):
+            Optional. Objective function the Model is to be optimized towards. The training
+            task creates a Model that maximizes/minimizes the value of the objective
+            function over the validation set.
+
+            The supported optimization objectives depend on the prediction type, and
+            in the case of classification also the number of distinct values in the
+            target column (two distint values -> binary, 3 or more distinct values
+            -> multi class).
+            If the field is not set, the default objective function is used.
+
+            Classification (binary):
+            "maximize-au-roc" (default) - Maximize the area under the receiver
+                                        operating characteristic (ROC) curve.
+            "minimize-log-loss" - Minimize log loss.
+            "maximize-au-prc" - Maximize the area under the precision-recall curve.
+            "maximize-precision-at-recall" - Maximize precision for a specified
+                                            recall value.
+            "maximize-recall-at-precision" - Maximize recall for a specified
+                                            precision value.
+
+            Classification (multi class):
+            "minimize-log-loss" (default) - Minimize log loss.
+
+            Regression:
+            "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
+            "minimize-mae" - Minimize mean-absolute error (MAE).
+            "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
+        column_transformations (Optional[Union[Dict, List[Dict]]]):
+            Optional. Transformations to apply to the input columns (i.e. columns other
+            than the targetColumn). Each transformation may produce multiple
+            result values from the column's value, and all are used for training.
+            When creating transformation for BigQuery Struct column, the column
+            should be flattened using "." as the delimiter.
+            If an input column has no transformations on it, such a column is
+            ignored by the training, except for the targetColumn, which should have
+            no transformations defined on.
+        optimization_objective_recall_value (float):
+            Optional. Required when maximize-precision-at-recall optimizationObjective was
+            picked, represents the recall value at which the optimization is done.
+
+            The minimum value is 0 and the maximum is 1.0.
+        optimization_objective_precision_value (float):
+            Optional. Required when maximize-recall-at-precision optimizationObjective was
+            picked, represents the precision value at which the optimization is
+            done.
+
+            The minimum value is 0 and the maximum is 1.0.
+
+    Returns:
+        model_name: Model name (fully-qualified)
+        model_dict: Model metadata in JSON format
+    '''
+
+    import datetime
+    import logging
+
+    from google.cloud import aiplatform
+    from google.protobuf import json_format
+
+    logging.getLogger().setLevel(logging.INFO)
+
+    if not model_display_name:
+        model_display_name = 'TablesModel_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S")
+
+    aiplatform.init(
+        project=project,
+        location=location,
+        encryption_spec_key_name=encryption_spec_key_name,
+    )
+    
+    model = aiplatform.AutoMLTabularTrainingJob(
+        display_name='AutoMLTabularTrainingJob_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S"),
+        optimization_prediction_type=optimization_prediction_type,
+        optimization_objective=optimization_objective,
+        #column_transformations=column_transformations,
+        optimization_objective_recall_value=optimization_objective_recall_value,
+        optimization_objective_precision_value=optimization_objective_precision_value,
+    ).run(
+        dataset=aiplatform.TabularDataset(dataset_name=dataset_name),
+        target_column=target_column,
+        training_fraction_split=training_fraction_split,
+        validation_fraction_split=validation_fraction_split,
+        test_fraction_split=test_fraction_split,
+        predefined_split_column_name=predefined_split_column_name,
+        weight_column=weight_column,
+        budget_milli_node_hours=budget_milli_node_hours,
+        model_display_name=model_display_name,
+        disable_early_stopping=disable_early_stopping,
+    )
+
+    (_, model_project, _, model_location, _, model_id) = model.resource_name.split('/')
+    model_web_url = f'https://console.cloud.google.com/vertex-ai/locations/{model_location}/models/{model_id}/evaluate?project={model_project}'
+    logging.info(f'Created model {model.name}.')
+    logging.info(f'Link: {model_web_url}')
+    model_json = json_format.MessageToJson(model._gca_resource._pb)
+    print(model_json)
+    return (model.resource_name, model_json, model_web_url)
+
+
+if __name__ == '__main__':
+    train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML_op = create_component_from_func(
+        train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML,
+        base_image='python:3.9',
+        # TODO: Update after my fix for TabularDataset.column_names bug is released https://github.com/googleapis/python-aiplatform/pull/590
+        packages_to_install=['git+https://github.com/googleapis/python-aiplatform.git@refs/pull/590/head'],
+        output_component_file='component.yaml',
+        annotations={
+            "author": "Alexey Volkov <alexey.volkov@ark-kun.com>",
+            "canonical_location": "https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml",
+        },
+    )
diff --git a/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml b/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml
new file mode 100644
index 0000000..3517ded
--- /dev/null
+++ b/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml
@@ -0,0 +1,524 @@
+name: Train tabular model using Google Cloud Vertex AI AutoML
+description: Trains model using Google Cloud Vertex AI AutoML.
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml'}
+inputs:
+- name: dataset_name
+  type: GoogleCloudVertexAiTabularDatasetName
+  description: |-
+    Required. The full name of dataset  (datasets.TabularDataset) within the same Project from which data will be used to train the Model. The
+    Dataset must use schema compatible with Model being trained,
+    and what is compatible should be described in the used
+    TrainingPipeline's [training_task_definition]
+    [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
+    For tabular Datasets, all their data is exported to
+    training, to pick and choose from.
+- {name: target_column, type: String, description: Required. The name of the column
+    values of which the Model is to predict.}
+- name: optimization_prediction_type
+  type: String
+  description: |-
+    The type of prediction the Model is to produce.
+    "classification" - Predict one out of multiple target values is
+    picked for each row.
+    "regression" - Predict a value based on its relation to other values.
+    This type is available only to columns that contain
+    semantically numeric values, i.e. integers or floating
+    point number, even if stored as e.g. strings.
+- name: training_fraction_split
+  type: Float
+  description: |-
+    Required. The fraction of the input data that is to be
+    used to train the Model. This is ignored if Dataset is not provided.
+  default: '0.8'
+  optional: true
+- name: validation_fraction_split
+  type: Float
+  description: |-
+    Required. The fraction of the input data that is to be
+    used to validate the Model. This is ignored if Dataset is not provided.
+  default: '0.1'
+  optional: true
+- name: test_fraction_split
+  type: Float
+  description: |-
+    Required. The fraction of the input data that is to be
+    used to evaluate the Model. This is ignored if Dataset is not provided.
+  default: '0.1'
+  optional: true
+- name: predefined_split_column_name
+  type: String
+  description: |-
+    Optional. The key is a name of one of the Dataset's data
+    columns. The value of the key (either the label's value or
+    value in the column) must be one of {``training``,
+    ``validation``, ``test``}, and it defines to which set the
+    given piece of data is assigned. If for a piece of data the
+    key is not present or has an invalid value, that piece is
+    ignored by the pipeline.
+
+    Supported only for tabular and time series Datasets.
+  optional: true
+- name: weight_column
+  type: String
+  description: |-
+    Optional. Name of the column that should be used as the weight column.
+    Higher values in this column give more importance to the row
+    during Model training. The column must have numeric values between 0 and
+    10000 inclusively, and 0 value means that the row is ignored.
+    If the weight column field is not set, then all rows are assumed to have
+    equal weight of 1.
+  optional: true
+- name: budget_milli_node_hours
+  type: Integer
+  description: |-
+    Optional. The train budget of creating this Model, expressed in milli node
+    hours i.e. 1,000 value in this field means 1 node hour.
+    The training cost of the model will not exceed this budget. The final
+    cost will be attempted to be close to the budget, though may end up
+    being (even) noticeably smaller - at the backend's discretion. This
+    especially may happen when further model training ceases to provide
+    any improvements.
+    If the budget is set to a value known to be insufficient to train a
+    Model for the given training set, the training won't be attempted and
+    will error.
+    The minimum value is 1000 and the maximum is 72000.
+  default: '1000'
+  optional: true
+- name: model_display_name
+  type: String
+  description: |-
+    Optional. If the script produces a managed Vertex AI Model. The display name of
+    the Model. The name can be up to 128 characters long and can be consist
+    of any UTF-8 characters.
+
+    If not provided upon creation, the job's display_name is used.
+  optional: true
+- name: disable_early_stopping
+  type: Boolean
+  description: |-
+    Required. If true, the entire budget is used. This disables the early stopping
+    feature. By default, the early stopping feature is enabled, which means
+    that training might stop before the entire training budget has been
+    used, if further training does no longer brings significant improvement
+    to the model.
+  default: "False"
+  optional: true
+- name: optimization_objective
+  type: String
+  description: |-
+    Optional. Objective function the Model is to be optimized towards. The training
+    task creates a Model that maximizes/minimizes the value of the objective
+    function over the validation set.
+
+    The supported optimization objectives depend on the prediction type, and
+    in the case of classification also the number of distinct values in the
+    target column (two distint values -> binary, 3 or more distinct values
+    -> multi class).
+    If the field is not set, the default objective function is used.
+
+    Classification (binary):
+    "maximize-au-roc" (default) - Maximize the area under the receiver
+                                operating characteristic (ROC) curve.
+    "minimize-log-loss" - Minimize log loss.
+    "maximize-au-prc" - Maximize the area under the precision-recall curve.
+    "maximize-precision-at-recall" - Maximize precision for a specified
+                                    recall value.
+    "maximize-recall-at-precision" - Maximize recall for a specified
+                                    precision value.
+
+    Classification (multi class):
+    "minimize-log-loss" (default) - Minimize log loss.
+
+    Regression:
+    "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
+    "minimize-mae" - Minimize mean-absolute error (MAE).
+    "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
+  optional: true
+- name: optimization_objective_recall_value
+  type: Float
+  description: |-
+    Optional. Required when maximize-precision-at-recall optimizationObjective was
+    picked, represents the recall value at which the optimization is done.
+
+    The minimum value is 0 and the maximum is 1.0.
+  optional: true
+- name: optimization_objective_precision_value
+  type: Float
+  description: |-
+    Optional. Required when maximize-recall-at-precision optimizationObjective was
+    picked, represents the precision value at which the optimization is
+    done.
+
+    The minimum value is 0 and the maximum is 1.0.
+  optional: true
+- {name: project, type: String, optional: true}
+- {name: location, type: String, default: us-central1, optional: true}
+- {name: encryption_spec_key_name, type: String, optional: true}
+outputs:
+- {name: model_name, type: GoogleCloudVertexAiModelName}
+- {name: model_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'git+https://github.com/googleapis/python-aiplatform.git@refs/pull/590/head'
+      || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'git+https://github.com/googleapis/python-aiplatform.git@refs/pull/590/head'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML(
+          # AutoMLTabularTrainingJob.run required parameters
+          dataset_name,
+          target_column,
+
+          # AutoMLTabularTrainingJob.__init__ required parameters
+          # display_name: str,
+          optimization_prediction_type,
+
+          # AutoMLTabularTrainingJob.run parameters
+          training_fraction_split = 0.8,
+          validation_fraction_split = 0.1,
+          test_fraction_split = 0.1,
+          predefined_split_column_name = None,
+          weight_column = None,
+          budget_milli_node_hours = 1000,
+          model_display_name = None,
+          disable_early_stopping = False,
+
+          # AutoMLTabularTrainingJob.__init__ parameters
+          optimization_objective = None,
+          #column_transformations: Union[Dict, List[Dict], NoneType] = None,
+          optimization_objective_recall_value = None,
+          optimization_objective_precision_value = None,
+
+          project = None,
+          location = 'us-central1',
+          #training_encryption_spec_key_name: str = None,
+          #model_encryption_spec_key_name: str = None,
+          encryption_spec_key_name = None,
+      ):
+          '''Trains model using Google Cloud Vertex AI AutoML.
+
+          Data fraction splits:
+          Any of ``training_fraction_split``, ``validation_fraction_split`` and
+          ``test_fraction_split`` may optionally be provided, they must sum to up to 1. If
+          the provided ones sum to less than 1, the remainder is assigned to sets as
+          decided by Vertex AI. If none of the fractions are set, by default roughly 80%
+          of data will be used for training, 10% for validation, and 10% for test.
+
+          Annotations:
+              author: Alexey Volkov <alexey.volkov@ark-kun.com>
+
+          Args:
+              dataset_name:
+                  Required. The full name of dataset  (datasets.TabularDataset) within the same Project from which data will be used to train the Model. The
+                  Dataset must use schema compatible with Model being trained,
+                  and what is compatible should be described in the used
+                  TrainingPipeline's [training_task_definition]
+                  [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
+                  For tabular Datasets, all their data is exported to
+                  training, to pick and choose from.
+              target_column (str):
+                  Required. The name of the column values of which the Model is to predict.
+              training_fraction_split (float):
+                  Required. The fraction of the input data that is to be
+                  used to train the Model. This is ignored if Dataset is not provided.
+              validation_fraction_split (float):
+                  Required. The fraction of the input data that is to be
+                  used to validate the Model. This is ignored if Dataset is not provided.
+              test_fraction_split (float):
+                  Required. The fraction of the input data that is to be
+                  used to evaluate the Model. This is ignored if Dataset is not provided.
+              predefined_split_column_name (str):
+                  Optional. The key is a name of one of the Dataset's data
+                  columns. The value of the key (either the label's value or
+                  value in the column) must be one of {``training``,
+                  ``validation``, ``test``}, and it defines to which set the
+                  given piece of data is assigned. If for a piece of data the
+                  key is not present or has an invalid value, that piece is
+                  ignored by the pipeline.
+
+                  Supported only for tabular and time series Datasets.
+              weight_column (str):
+                  Optional. Name of the column that should be used as the weight column.
+                  Higher values in this column give more importance to the row
+                  during Model training. The column must have numeric values between 0 and
+                  10000 inclusively, and 0 value means that the row is ignored.
+                  If the weight column field is not set, then all rows are assumed to have
+                  equal weight of 1.
+              budget_milli_node_hours (int):
+                  Optional. The train budget of creating this Model, expressed in milli node
+                  hours i.e. 1,000 value in this field means 1 node hour.
+                  The training cost of the model will not exceed this budget. The final
+                  cost will be attempted to be close to the budget, though may end up
+                  being (even) noticeably smaller - at the backend's discretion. This
+                  especially may happen when further model training ceases to provide
+                  any improvements.
+                  If the budget is set to a value known to be insufficient to train a
+                  Model for the given training set, the training won't be attempted and
+                  will error.
+                  The minimum value is 1000 and the maximum is 72000.
+              model_display_name (str):
+                  Optional. If the script produces a managed Vertex AI Model. The display name of
+                  the Model. The name can be up to 128 characters long and can be consist
+                  of any UTF-8 characters.
+
+                  If not provided upon creation, the job's display_name is used.
+              disable_early_stopping (bool):
+                  Required. If true, the entire budget is used. This disables the early stopping
+                  feature. By default, the early stopping feature is enabled, which means
+                  that training might stop before the entire training budget has been
+                  used, if further training does no longer brings significant improvement
+                  to the model.
+
+              optimization_prediction_type (str):
+                  The type of prediction the Model is to produce.
+                  "classification" - Predict one out of multiple target values is
+                  picked for each row.
+                  "regression" - Predict a value based on its relation to other values.
+                  This type is available only to columns that contain
+                  semantically numeric values, i.e. integers or floating
+                  point number, even if stored as e.g. strings.
+              optimization_objective (str):
+                  Optional. Objective function the Model is to be optimized towards. The training
+                  task creates a Model that maximizes/minimizes the value of the objective
+                  function over the validation set.
+
+                  The supported optimization objectives depend on the prediction type, and
+                  in the case of classification also the number of distinct values in the
+                  target column (two distint values -> binary, 3 or more distinct values
+                  -> multi class).
+                  If the field is not set, the default objective function is used.
+
+                  Classification (binary):
+                  "maximize-au-roc" (default) - Maximize the area under the receiver
+                                              operating characteristic (ROC) curve.
+                  "minimize-log-loss" - Minimize log loss.
+                  "maximize-au-prc" - Maximize the area under the precision-recall curve.
+                  "maximize-precision-at-recall" - Maximize precision for a specified
+                                                  recall value.
+                  "maximize-recall-at-precision" - Maximize recall for a specified
+                                                  precision value.
+
+                  Classification (multi class):
+                  "minimize-log-loss" (default) - Minimize log loss.
+
+                  Regression:
+                  "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
+                  "minimize-mae" - Minimize mean-absolute error (MAE).
+                  "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
+              column_transformations (Optional[Union[Dict, List[Dict]]]):
+                  Optional. Transformations to apply to the input columns (i.e. columns other
+                  than the targetColumn). Each transformation may produce multiple
+                  result values from the column's value, and all are used for training.
+                  When creating transformation for BigQuery Struct column, the column
+                  should be flattened using "." as the delimiter.
+                  If an input column has no transformations on it, such a column is
+                  ignored by the training, except for the targetColumn, which should have
+                  no transformations defined on.
+              optimization_objective_recall_value (float):
+                  Optional. Required when maximize-precision-at-recall optimizationObjective was
+                  picked, represents the recall value at which the optimization is done.
+
+                  The minimum value is 0 and the maximum is 1.0.
+              optimization_objective_precision_value (float):
+                  Optional. Required when maximize-recall-at-precision optimizationObjective was
+                  picked, represents the precision value at which the optimization is
+                  done.
+
+                  The minimum value is 0 and the maximum is 1.0.
+
+          Returns:
+              model_name: Model name (fully-qualified)
+              model_dict: Model metadata in JSON format
+          '''
+
+          import datetime
+          import logging
+
+          from google.cloud import aiplatform
+          from google.protobuf import json_format
+
+          logging.getLogger().setLevel(logging.INFO)
+
+          if not model_display_name:
+              model_display_name = 'TablesModel_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S")
+
+          aiplatform.init(
+              project=project,
+              location=location,
+              encryption_spec_key_name=encryption_spec_key_name,
+          )
+
+          model = aiplatform.AutoMLTabularTrainingJob(
+              display_name='AutoMLTabularTrainingJob_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S"),
+              optimization_prediction_type=optimization_prediction_type,
+              optimization_objective=optimization_objective,
+              #column_transformations=column_transformations,
+              optimization_objective_recall_value=optimization_objective_recall_value,
+              optimization_objective_precision_value=optimization_objective_precision_value,
+          ).run(
+              dataset=aiplatform.TabularDataset(dataset_name=dataset_name),
+              target_column=target_column,
+              training_fraction_split=training_fraction_split,
+              validation_fraction_split=validation_fraction_split,
+              test_fraction_split=test_fraction_split,
+              predefined_split_column_name=predefined_split_column_name,
+              weight_column=weight_column,
+              budget_milli_node_hours=budget_milli_node_hours,
+              model_display_name=model_display_name,
+              disable_early_stopping=disable_early_stopping,
+          )
+
+          (_, model_project, _, model_location, _, model_id) = model.resource_name.split('/')
+          model_web_url = f'https://console.cloud.google.com/vertex-ai/locations/{model_location}/models/{model_id}/evaluate?project={model_project}'
+          logging.info(f'Created model {model.name}.')
+          logging.info(f'Link: {model_web_url}')
+          model_json = json_format.MessageToJson(model._gca_resource._pb)
+          print(model_json)
+          return (model.resource_name, model_json, model_web_url)
+
+      def _deserialize_bool(s) -> bool:
+          from distutils.util import strtobool
+          return strtobool(s) == 1
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train tabular model using Google Cloud Vertex AI AutoML', description='Trains model using Google Cloud Vertex AI AutoML.')
+      _parser.add_argument("--dataset-name", dest="dataset_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--target-column", dest="target_column", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimization-prediction-type", dest="optimization_prediction_type", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--training-fraction-split", dest="training_fraction_split", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--validation-fraction-split", dest="validation_fraction_split", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--test-fraction-split", dest="test_fraction_split", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--predefined-split-column-name", dest="predefined_split_column_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--weight-column", dest="weight_column", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--budget-milli-node-hours", dest="budget_milli_node_hours", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--model-display-name", dest="model_display_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--disable-early-stopping", dest="disable_early_stopping", type=_deserialize_bool, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimization-objective", dest="optimization_objective", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimization-objective-recall-value", dest="optimization_objective_recall_value", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimization-objective-precision-value", dest="optimization_objective_precision_value", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--project", dest="project", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--location", dest="location", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--encryption-spec-key-name", dest="encryption_spec_key_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML(**_parsed_args)
+
+      _output_serializers = [
+          str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --dataset-name
+    - {inputValue: dataset_name}
+    - --target-column
+    - {inputValue: target_column}
+    - --optimization-prediction-type
+    - {inputValue: optimization_prediction_type}
+    - if:
+        cond: {isPresent: training_fraction_split}
+        then:
+        - --training-fraction-split
+        - {inputValue: training_fraction_split}
+    - if:
+        cond: {isPresent: validation_fraction_split}
+        then:
+        - --validation-fraction-split
+        - {inputValue: validation_fraction_split}
+    - if:
+        cond: {isPresent: test_fraction_split}
+        then:
+        - --test-fraction-split
+        - {inputValue: test_fraction_split}
+    - if:
+        cond: {isPresent: predefined_split_column_name}
+        then:
+        - --predefined-split-column-name
+        - {inputValue: predefined_split_column_name}
+    - if:
+        cond: {isPresent: weight_column}
+        then:
+        - --weight-column
+        - {inputValue: weight_column}
+    - if:
+        cond: {isPresent: budget_milli_node_hours}
+        then:
+        - --budget-milli-node-hours
+        - {inputValue: budget_milli_node_hours}
+    - if:
+        cond: {isPresent: model_display_name}
+        then:
+        - --model-display-name
+        - {inputValue: model_display_name}
+    - if:
+        cond: {isPresent: disable_early_stopping}
+        then:
+        - --disable-early-stopping
+        - {inputValue: disable_early_stopping}
+    - if:
+        cond: {isPresent: optimization_objective}
+        then:
+        - --optimization-objective
+        - {inputValue: optimization_objective}
+    - if:
+        cond: {isPresent: optimization_objective_recall_value}
+        then:
+        - --optimization-objective-recall-value
+        - {inputValue: optimization_objective_recall_value}
+    - if:
+        cond: {isPresent: optimization_objective_precision_value}
+        then:
+        - --optimization-objective-precision-value
+        - {inputValue: optimization_objective_precision_value}
+    - if:
+        cond: {isPresent: project}
+        then:
+        - --project
+        - {inputValue: project}
+    - if:
+        cond: {isPresent: location}
+        then:
+        - --location
+        - {inputValue: location}
+    - if:
+        cond: {isPresent: encryption_spec_key_name}
+        then:
+        - --encryption-spec-key-name
+        - {inputValue: encryption_spec_key_name}
+    - '----output-paths'
+    - {outputPath: model_name}
+    - {outputPath: model_dict}