Here is an example of how to use BigQuery ML on a public dataset to create a logistic regression model to predict whether a user will click on an ad: # Import the BigQuery ML library from google.cloud import bigquery from google.cloud.bigquery import Model # Get the dataset and table dataset = bigquery.Dataset("bigquery-public-data.samples.churn") table = dataset.table("churn") # Create a model model = Model('my_model', model_type='logistic_regression', input_label_column='churn', input_features_columns=['tenure', 'contract', 'monthly_charges']) # Train the model model.

BigQuery ML and Vertex AI Generative AI (GenAI) are both machine learning (ML) services that can be used to build and deploy ML models. However, there are some key differences between the two services. BigQuery ML: BigQuery ML is a fully managed ML service that allows you to build and deploy ML models without having to manage any infrastructure. BigQuery ML uses the same machine learning algorithms as Vertex AI, but it does not offer the same level of flexibility or control.