Vertex AI Model Registry

Related topics

• 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.”
• Data Catalog works with Vertex AI Model Registry From the official documentation: “Vertex AI Model Registry Models and managed datasets are now synced to Dataplex's Data Catalog service.”
• BigQuery works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze BigQuery data.”
• 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: “External BigQuery data source backed by Cloud Storage”
• BigQuery works with Knowledge Catalog From the official documentation: “Some Knowledge Catalog functionalities trigger job execution using BigQuery.”
• Pub/Sub works with Sensitive Data Protection From the official documentation: “Watch the Pub/Sub topic until the DLP job finishes”
• BigQuery is an alternative to Pub/Sub From the official documentation: “To keep audit logs for a longer period of time or to use more powerful search capabilities, you can route copies of your audit logs to Cloud Storage, BigQuery, or Pub/Sub.”
• 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.”
• Cloud Storage works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze a file on GCS.”
• 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 works with Managed Service for Apache Spark From the official documentation: “This means you can quickly read and write BigQuery data to and from Managed Service for Apache Spark.”
• BigQuery works with Pub/Sub From the official documentation: “createBigQuerySubscription creates a Pub/Sub subscription that exports messages to BigQuery.”
• Cloud Storage works with Pub/Sub From the official documentation: “| **Google Cloud** | Cloud Storage | Pub/Sub |”
• Cloud Storage is an alternative to Pub/Sub From the official documentation: “To keep audit logs for a longer period of time or to use more powerful search capabilities, you can route copies of your audit logs to Cloud Storage, BigQuery, or Pub/Sub.”
• BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “Run queries against your federated data using BigQuery or Managed Service for Apache Spark.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• Cloud Storage works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark accesses these files on Cloud Storage.”
• Data Catalog is an alternative to Knowledge Catalog From the official documentation: “Use Knowledge Catalog for all new projects to take advantage of AI-powered features and enhanced governance.”
• BigQuery ML is part of BigQuery From the official documentation: “BigQuery ML lets you use SQL in BigQuery to perform machine learning (ML) and predictive analytics.”
• Data Catalog is part of Knowledge Catalog From the official documentation: “Knowledge Catalog features”
• Managed Service for Apache Spark depends on Cloud Storage From the official documentation: “Managed Service for Apache Spark uses Cloud Storage as its default distributed file system.”
• BigQuery works with Data Catalog From the official documentation: “The instructions in this page use both BigQuery and Data Catalog.”
• Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your image AutoML Edge models to Cloud Storage.”
• Data Catalog works with Sensitive Data Protection From the official documentation: “Send job results to Data Catalog. This feature is [deprecated](/sensitive-data-protection/docs/deprecations).”
• Knowledge Catalog works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark integrates with other Google Cloud products, such as BigQuery and Knowledge Catalog.”
• Knowledge Catalog works with Sensitive Data Protection From the official documentation: “Send the results of a [BigQuery inspection job](/sensitive-data-protection/docs/inspecting-storage#inspect_a_table) to Knowledge Catalog.”
• 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 an alternative to Managed Service for Apache Spark From the official documentation: “Use BigQuery ML, BigQuery DataFrames, or Managed Service for Apache Spark to perform distributed data processing on large datasets.”
• Cloud Storage works with Data Catalog From the official documentation: “Data Catalog supports the definition of Cloud Storage file entries and their schema.”
• Cloud Storage works with Knowledge Catalog From the official documentation: “To automatically discover and build inventories of the data assets, integrate Knowledge Catalog with your storage systems, such as [BigQuery](/bigquery/docs/introduction), [Cloud Storage](/storage/docs/introduction), and [Pub/Sub](/pubsub/docs/overview).”
• Data Catalog works with Pub/Sub From the official documentation: “Pub/Sub topics are automatically registered with the Data Catalog.”
• 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 Knowledge Catalog From the official documentation: “Knowledge Catalog provides a way to search for your Agent Platform models across projects and regions.”
• Knowledge Catalog depends on Cloud Storage From the official documentation: “Looker (Google Cloud core) publishes LookML metadata to regional Cloud Storage repositories for Knowledge Catalog to consume through a batch sync process.”
• Knowledge Catalog works with Pub/Sub From the official documentation: “To automatically discover and build inventories of the data assets, integrate Knowledge Catalog with your storage systems, such as [BigQuery](/bigquery/docs/introduction), [Cloud Storage](/storage/docs/introduction), and [Pub/Sub](/pubsub/docs/overview).”
• Managed Service for Apache Spark works with Pub/Sub From the official documentation: “You can use Managed Service for Apache Spark to process high-volume streams from a service like Pub/Sub or Apache Kafka and land the data in the bronze tier.”
• Sensitive Data Protection secures BigQuery From the official documentation: “To help meet compliance requirements, use Sensitive Data Protection to inspect your BigQuery tables and to help protect your sensitive data.”