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BigQuery ML works with Vertex AI Model Registry From the official documentation: “The **Vertex AI Model Registry and BigQuery ML** integration is generally available ([GA](https://cloud.google.com/products#product-launch-stages)).”
Cloud Storage works with Vertex AI Model Registry From the official documentation: “Upload the model saved in the Cloud Storage bucket to Vertex AI Model Registry.”
BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
BigQuery works with Cloud Storage From the official documentation: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
BigQuery ML works with Cloud Storage From the official documentation: “You can export BigQuery ML models to Cloud Storage, and use them for online prediction, or edit them in Python.”
BigQuery works with Gemini Enterprise Agent Platform From the official documentation: “The BigQuery dataset stores all versions for the associated Gemini Enterprise Agent Platform dataset.”
Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “When you perform custom training, Agent Platform usually loads your data from Cloud Storage.”
BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
BigQuery works with BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
BigQuery ML is part of BigQuery From the official documentation: “BigQuery ML models are stored in BigQuery datasets, similar to tables and views.”
BigQuery ML works with Cloud KMS key From the official documentation: “BigQuery ML supports CMEK.”
BigQuery ML depends on Cloud Storage From the official documentation: “BigQuery ML imports the model from Cloud Storage by using the credentials of the user who runs the `CREATE MODEL` statement.”
BigQuery ML is an alternative to Gemini Enterprise Agent Platform From the official documentation: “You can use [BigQuery ML](/bigquery/docs/bqml-introduction) or [Agent Platform development tools](/gemini-enterprise-agent-platform/machine-learning/general/developer-tools-overview) to develop, train, and evaluate ML models.”
Cloud KMS key secures BigQuery From the official documentation: “Protect a new table with a customer-managed encryption key by setting the [Table.encryption_configuration](/python/docs/reference/bigquery/latest/google.cloud.bigquery.table.Table#google_cloud_bigquery_table_Table_encryption_configuration) property to an…”
Cloud KMS key secures Cloud Storage From the official documentation: “Run the [`gcloud storage batch-operations jobs create`](/sdk/gcloud/reference/storage/batch-operations/jobs/create) command to [create a Storage batch operations job by using the manifest…”
Gemini Enterprise Agent Platform depends on Cloud Storage From the official documentation: “Certain tasks in Agent Platform require that you use additional Google Cloud products besides Agent Platform. For example, in most cases, you must use Cloud Storage and Artifact Registry when you create a custom training pipeline.”