Demystifying Vertex AI BigQuery Model Creation Job's Query Parameters Format
Vertex AI, Google Cloud's powerful machine learning platform, offers a robust set of tools for building and deploying models. One of its key features is the ability to create models directly from BigQuery data using the CreateModelJobOp operation. This process involves defining a query that extracts the desired data for model training, and the query_parameters field plays a crucial role in this process.
Understanding the Problem:
While the Vertex AI documentation mentions the query_parameters field, it lacks detailed information about its expected format. This lack of clarity can be frustrating for developers trying to leverage this functionality.
Rephrasing the Problem:
How do we correctly format the query_parameters field when creating a BigQuery-based model in Vertex AI? What are the accepted data types and how should they be structured?
Illustrative Scenario and Code:
Let's imagine you have a BigQuery table named customer_data containing information about customer demographics and purchasing behavior. You aim to build a model that predicts customer churn using Vertex AI.
Here's a simplified code snippet for creating a model using the CreateModelJobOp:
from google.cloud import aiplatform
# Create a Model Training Job
model_job = aiplatform.ModelTrainingJob(
display_name="customer_churn_model",
model_type="AUTOML_TABLE",
prediction_type="classification",
training_data_source=aiplatform.TrainingDataSource(
bigquery_source=aiplatform.BigQuerySource(
project_id="your_project_id",
dataset_id="your_dataset_id",
table_id="customer_data",
query_parameters={
"threshold": 0.5,
"customer_type": "premium",
}
)
),
# ... other parameters
)
# Run the job
model_job.run()
Analyzing the query_parameters Format:
The query_parameters field within BigQuerySource is a dictionary that allows you to pass parameters to your BigQuery query. These parameters can be used for:
- Filtering data: Defining conditions for selecting specific data points (e.g.,
customer_typein the example). - Controlling query behavior: Adjusting parameters like thresholds for classification tasks.
Key Considerations:
- Data Types: The
query_parametersdictionary accepts values of different data types, including strings, numbers, booleans, and lists. - Query Integration: The parameter values are injected into your BigQuery query using placeholders. You can use the
@symbol followed by the parameter name within your query, for example:
SELECT * FROM `your_project_id.your_dataset_id.customer_data`
WHERE customer_type = @customer_type
AND churn_probability > @threshold;
- Parameter Substitution: Vertex AI automatically substitutes the values in the
query_parametersdictionary with the corresponding placeholders in your BigQuery query.
Example Scenarios:
-
Filtering by Date Range:
query_parameters = { "start_date": "2023-01-01", "end_date": "2023-12-31", }SELECT * FROM `your_project_id.your_dataset_id.customer_data` WHERE purchase_date BETWEEN @start_date AND @end_date; -
Setting a Threshold for Classification:
query_parameters = { "threshold": 0.7, }SELECT * FROM `your_project_id.your_dataset_id.customer_data` WHERE churn_probability > @threshold;
Benefits of Using query_parameters:
- Increased Flexibility: Dynamically control data selection and query behavior based on different requirements.
- Improved Code Readability: Separate query logic from parameter values, leading to cleaner code.
- Enhanced Reusability: Easily modify parameters without altering the underlying query.
Conclusion:
Understanding the query_parameters format is essential for effectively utilizing Vertex AI's BigQuery-based model creation capabilities. By leveraging this feature, you can customize data extraction, fine-tune query behavior, and build more robust machine learning models.
References:
