CREATE MODEL statement

Related topics

• CREATE MODEL statement is part of BigQuery From the official documentation: “This document describes the `CREATE MODEL` statement for creating [random forest](https://en.wikipedia.org/wiki/Random_forest) models in BigQuery by using SQL.”
• CREATE MODEL statement configures model From the official documentation: “Creates and trains a new model in the specified dataset. If the model name exists, `CREATE MODEL` returns an error.”
• CREATE MODEL statement is part of BigQuery ML From the official documentation: “To create a model in BigQuery, use the BigQuery ML `CREATE MODEL` statement.”
• CREATE MODEL statement configures remote model From the official documentation: “This document describes the `CREATE MODEL` statement for fine-tuning Gemini models in Gemini Enterprise Agent Platform by using SQL.”
• 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”
• Customer-Managed Encryption Keys secures Cloud Storage From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
• 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.”
• BigQuery dataset is part of BigQuery From the official documentation: “Sample demonstrating how to create a dataset in BigQuery.”
• Cloud Storage works with Customer-Managed Encryption Keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• Customer-Managed Encryption Keys secures BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
• BigQuery works with BigQuery DataFrames From the official documentation: “Uploads a pandas DataFrame to BigQuery using BigQuery DataFrames.”
• Cloud resource connection works with Cloud Storage From the official documentation: “Cloud resource connection lets BigQuery access Cloud Storage object data and metadata using its own service account.”
• Cloud resource connection works with Gemini Enterprise Agent Platform From the official documentation: “BigQuery uses a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) to interact with the Gemini Enterprise Agent Platform endpoint.”
• 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.”
• Customer-Managed Encryption Keys secures Gemini Enterprise Agent Platform From the official documentation: “Enable customer-managed encryption keys (CMEK) for the Gemini Enterprise Agent Platform to gain more control over data encryption and key management.”
• remote model is part of BigQuery ML From the official documentation: “- Create a BigQuery ML remote model.”
• BigQuery works with Cloud resource connection From the official documentation: “After you create the training dataset, you establish a connection to link BigQuery and external sources.”
• BigQuery dataset works with Cloud resource connection From the official documentation: “Create the connection in the same [location](/bigquery/docs/locations) as the dataset you created in the previous step.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• Cloud resource connection is part of BigQuery From the official documentation: “A Cloud resource connection in BigQuery lets you connect BigQuery to other Google Cloud services and external storage by using a dedicated service account with delegated access.”
• Customer-Managed Encryption Keys depends on Cloud Storage From the official documentation: “If used for storing Customer-managed Encryption Keys (CMEK): Cloud Storage (See [Cloud Storage pricing](/storage/pricing).)”
• remote model depends on Gemini Enterprise Agent Platform From the official documentation: “Create a remote model that represents a hosted Agent Platform model:”
• 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 resource connection From the official documentation: “BigQuery uses a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) to interact with the Cloud AI service.”
• 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 works with remote model From the official documentation: “The function uses a BigQuery ML [remote model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-service) to connect BigQuery to the Cloud Natural Language API.”
• BigQuery DataFrames depends on BigQuery From the official documentation: “BigQuery DataFrames uses BigQuery, Cloud Run functions, Agent Platform, and other Google Cloud services, which incur their own costs.”
• BigQuery DataFrames works with BigQuery ML From the official documentation: “Offers more than 750 pandas and scikit-learn APIs implemented through SQL conversion to BigQuery and BigQuery ML APIs.”
• 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 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 resource connection depends on Cloud Storage From the official documentation: “- `CONNECTION_ID`: A `STRING` value that contains a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) that the service can use to access the objects in Cloud Storage, in the format `location.connection_id`.”
• Cloud resource connection works with remote model From the official documentation: “Create a [Cloud resource connection](/bigquery/docs/create-cloud-resource-connection) for the remote model to use, and get the connection's service account.”
• Customer-Managed Encryption Keys is part of BigQuery From the official documentation: “If you require more control over encryption keys, BigQuery supports [customer-managed encryption keys](/bigquery/docs/customer-managed-encryption) in the [Cloud Key Management Service](/kms/docs).”
• Customer-Managed Encryption Keys is part of Cloud Storage From the official documentation: “[Cloud Storage](/storage/docs/introduction) provides features such as customer-managed encryption, lifecycle management, and aging and retention policies.”
• Customer-Managed Encryption Keys secures model From the official documentation: “For **models**, the models stored in the storage system (for example, disk) are CMEK-encrypted.”
• 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.”
• Gemini Enterprise Agent Platform works with remote model From the official documentation: “Create a remote model that represents a hosted Agent Platform `gemini-3.5-flash-lite` model:”
• model is part of BigQuery dataset From the official documentation: “Delete a model from a dataset.”
• model is part of Gemini Enterprise Agent Platform From the official documentation: “`Model`, this resource is available in Gemini Enterprise Agent Platform.”