This guide describes how to connect Aporia to a BigQuery data source in order to monitor a new ML Model in production.

We will assume that your model inputs, outputs and optionally delayed actuals are stored in a BigQuery table, or can be queried with a BigQuery view.

The BigQuery data source may also be used to connect to your model's training/test set to be used as a baseline for model monitoring.

Creating a service account

First, create a read-only service account for Aporia:

  1. Under IAM & Admin, go to the Service Accounts section in your Google Cloud Platform console.

  2. Click the Create Service Account button at the top of the tab.

  3. Give the account a name and continue. We recommend naming the account "aporia".

  4. Assign the roles/bigquery.jobUser role to the service account.

  5. Click the Create Key button, select JSON as the type and click Create. A JSON file will be downloaded – please keep it safe.

  6. Click Done to complete the creation of Aporia’s service account.

Next, add permissions to the relevant tables / views:

  1. Go to the BigQuery service in your Google Cloud Platform console.

  2. In the Explorer panel, expand your project and select a dataset.

  3. Expand the dataset and select a table or view.

  4. Click Share.

  5. On the Share tab, Click Add Principal.

  6. In New principals, enter the name of the Service Account you've created for Aporia in the previous step.

  7. Select the roles/bigquery.dataViewer role.

  8. Click Save to save the changes for the new user.

ServiceAccount credentials

For authentication without service account credentials, please contact your Aporia account manager.

Creating a BigQuery data source in Aporia

To create a new model to be monitored in Aporia, you can call the aporia.create_model(...) API:

aporia.create_model("<MODEL_ID>", "<MODEL_NAME>")

Each model in Aporia contains different Model Versions. When you (re)train your model, you should create a new model version in Aporia.

apr_model = aporia.create_model_version(
    "raw_text": "text",

    "amount": "numeric",
    "owner": "string",
    "is_new": "boolean",
    "embeddings": {"type": "tensor", "dimensions": [768]},

    "will_buy_insurance": "boolean",
    "proba": "numeric",

Each raw input, feature or prediction is mapped by default to the column of the same name in the BigQuery table or view.

By creating a feature named amount or a prediction named proba, for example, the BigQuery data source will expect a column in the BigQuery table named amount or proba, respectively.

If your data format does not fit exactly, you can use BigQuery Views to shape it in any way you want.

Next, create an instance of BigQueryDataSource and pass it to apr_model.connect_serving(...) or apr_model.connect_training(...):

data_source = BigQueryDataSource(

  # Instead of table, you can also use a BigQuery view for custom queries
  dataset="<DATASET>",                     # Optional
  project="<PROJECT_NAME>",                # Optional
  parent_project="<PARENT_PROJECT_NAME>",  # Optional

  # Optional - use the select_expr param to apply additional Spark SQL 
  select_expr=["<SPARK_SQL>", ...],

  # Optional - use the read_options param to apply any Spark configuration
  # (e.g custom Spark resources necessary for this model)


  # Names of the prediction ID and prediction timestamp columns

Note that as part of the connect_serving API, you are required to specify additional 2 columns:

  • id_column - A unique ID to represent this prediction.

  • timestamp_column - A column representing when did this prediction occur.

What's Next

For more information on:

  • Advanced feature / prediction <-> column mapping

  • How to integrate delayed actuals

  • How to integrate training / test sets

Please see the Data Sources Overview page.

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