1. BigQuery as AI Model Evaluation Data Repository


    BigQuery is a fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. It's a Platform as a Service (PaaS) that supports querying using ANSI SQL. It also has built-in machine learning capabilities which allow you to create and execute machine learning models on large datasets.

    If you wish to use BigQuery as a repository for AI model evaluation data, you typically need to do the following:

    1. Create a BigQuery Dataset: Datasets in BigQuery organize and control access to your tables.
    2. Create a BigQuery Table: Tables hold the data within a dataset.
    3. Define a schema for the table: The schema specifies the column names, types, and other information.
    4. Insert evaluation data: You can stream data into BigQuery or use a batch loading process.
    5. Query the data: Use SQL queries to retrieve the data needed for evaluating your AI models.

    For the purposes of setting this up using Pulumi, we'll go through setting up a dataset and table with a simple schema, which would be where your AI model evaluation data would be stored. Let's take a closer look at how we can do this in Python using Pulumi's GCP provider.

    import pulumi import pulumi_gcp as gcp # Provide your GCP project and desired region gcp_project = 'your-gcp-project' gcp_region = 'your-gcp-region' # Create a BigQuery dataset to store your AI model evaluation data ai_dataset = gcp.bigquery.Dataset("ai_evaluation_dataset", dataset_id="ai_evaluation_data", description="Dataset to store AI model evaluation data", location=gcp_region, project=gcp_project, labels={"env": "production"}) # Setting labels for environment identification # Define the schema for the BigQuery table based on the evaluation data you expect ai_table_schema = [ gcp.bigquery.DatasetTableFieldArgs( name="model_name", type="STRING", description="Name of the AI model", ), gcp.bigquery.DatasetTableFieldArgs( name="evaluation_metric", type="FLOAT", description="Metric score for model evaluation", ), gcp.bigquery.DatasetTableFieldArgs( name="data_split", type="STRING", description="Data split used (e.g., 'train', 'validation', 'test')", ), # Add additional fields based on your needs ] # Create a BigQuery table inside our dataset with the defined schema ai_evaluation_table = gcp.bigquery.Table("ai_evaluation_table", dataset_id=ai_dataset.dataset_id, table_id="model_evaluation", project=gcp_project, deletion_protection=False, # Allows the table to be deleted. Set to True for production environments. schema=ai_table_schema) # Export the fully qualified dataset and table names which will be used as identifiers pulumi.export('dataset_id', ai_dataset.dataset_id) pulumi.export('table_id', ai_evaluation_table.table_id)

    This Pulumi program sets up a BigQuery dataset and table where you can store and query your AI model evaluation data.

    • We start by importing the required modules from Pulumi and defining the GCP project and region.
    • Next, we create a BigQuery dataset using gcp.bigquery.Dataset, specifying details like dataset_id, description, location, and labels.
    • We then declare a schema for the table with gcp.bigquery.DatasetTableFieldArgs for each field we want to store. In this example, we have model_name, evaluation_metric, and data_split.
    • After that, we create a BigQuery table within our dataset using gcp.bigquery.Table, where we provide our dataset ID, table ID, project, and schema.
    • Finally, we export the dataset and table identifiers using Pulumi's export function for easy reference later.

    With this structure in place, you can proceed to add the data ingestion mechanisms and querying functionality per your AI model evaluation requirements.