Vertex AI Feature Store

Related topics

• BigQuery works with Vertex AI Feature Store From the official documentation: “In the new Vertex AI Feature Store, BigQuery serves as the offline store.”
• Optimized online serving is part of Vertex AI Feature Store From the official documentation: “Vertex AI Feature Store offers the following types of online serving that you can use to serve features for online predictions: - [Bigtable online serving](#bigtable_serving) - [Optimized online serving](#optimized_serving)”
• Bigtable online serving is part of Vertex AI Feature Store From the official documentation: “In this tutorial, you learn how to use Bigtable online serving in Vertex AI Feature Store for online serving and fetching of feature values in BigQuery.”
• Cloud Monitoring works with Vertex AI Feature Store From the official documentation: “In Cloud Monitoring, the monitored resource type for an online store instance is [`aiplatform.googleapis.com/FeatureOnlineStore`](/monitoring/api/resources#tag_aiplatform.googleapis.com/FeatureOnlineStore).”
• Data Catalog works with Vertex AI Feature Store From the official documentation: “Data Catalog catalogs metadata from Vertex AI Feature Store resources.”
• Knowledge Catalog works with Vertex AI Feature Store From the official documentation: “Knowledge Catalog is also integrated with Vertex AI Feature Store, offering similar capabilities.”
• online store is part of Vertex AI Feature Store From the official documentation: “To enable online serving of features, use the Vertex AI Feature Store `CreateFeatureOnlineStore` API to set up a `FeatureOnlineStore` instance.”
• Vertex AI Feature Store depends on ML.TFDV_VALIDATE From the official documentation: “Vertex AI Feature Store compares the snapshots from consecutive feature monitor job executions and calculates drifts using [the ML.TFDV_VALIDATE function in BigQuery](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-validate).”
• 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 Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
• VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
• BigQuery works with Knowledge Catalog From the official documentation: “Some Knowledge Catalog functionalities trigger job execution using BigQuery.”
• BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to 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.”
• Agent Platform Feature Store works with BigQuery From the official documentation: “In this tutorial, you learn how to use Bigtable online serving in Vertex AI Feature Store for online serving and fetching of feature values in BigQuery.”
• 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 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.”
• VPC Service Controls secures Cloud Storage From the official documentation: “Storage Transfer Service supports transfers to Cloud Storage buckets protected by VPC Service Controls.”
• Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
• Cloud Logging works with Pub/Sub From the official documentation: “Pub/Sub writes various log entries to Cloud Logging in the Google Cloud console.”
• 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 depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Cloud Monitoring works with Pub/Sub From the official documentation: “Use Cloud Monitoring and Pub/Sub to set up custom monitoring and alerting policies for exported logs.”
• Data Catalog is part of Knowledge Catalog From the official documentation: “Knowledge Catalog features”
• BigQuery works with Data Catalog From the official documentation: “The instructions in this page use both BigQuery and Data Catalog.”
• Bigtable online serving is an alternative to Optimized online serving From the official documentation: “Depending on the type of online serving you've configured for your online store, you can serve feature values in one of the following ways: - Fetch feature values by using Bigtable online serving. - Fetch feature values by using Optimized online serving with a public endpoint.”
• Cloud Logging works with Cloud Monitoring From the official documentation: “If a log entry matches the filter of the alerting policy, then Cloud Logging extracts labels and forwards the data to Cloud Monitoring.”
• 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 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.”
• Sensitive Data Protection works with VPC Service Controls From the official documentation: “For information about VPC Service Controls support for Sensitive Data Protection, see the [Supported products](/vpc-service-controls/docs/supported-products#table_dlp) table in the VPC Service Controls documentation.”
• VPC Service Controls secures Knowledge Catalog From the official documentation: “The API for Knowledge Catalog can be protected by VPC Service Controls and the product can be used normally inside service perimeters.”
• Agent Platform Feature Store works with Knowledge Catalog From the official documentation: “Vertex AI Feature Store is integrated with Knowledge Catalog to track feature metadata.”
• BigQuery works with Bigtable online serving From the official documentation: “If you want to use continuous data sync to sync data from the BigQuery data source to your feature views in near real-time, use Bigtable online serving.”
• 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 VPC Service Controls From the official documentation: “BigQuery integrates with VPC Service Controls to restrict access to your BigQuery resources from specific networks.”
• Bigtable online serving is part of Agent Platform Feature Store From the official documentation: “In this tutorial, you learn how to use Bigtable online serving in Vertex AI Feature Store for online serving and fetching of feature values in BigQuery.”
• Cloud Logging is an alternative to Cloud Monitoring From the official documentation: “To see the status of your invalidation request or find a definitive list of recently submitted requests, you can use Cloud Logging or Cloud Monitoring.”
• Cloud Logging is an alternative to Cloud Storage From the official documentation: “When you specify your own service account for builds, you must store your build logs either in Cloud Logging or in a user-created Cloud Storage bucket.”
• Cloud Logging works with Knowledge Catalog From the official documentation: “Knowledge Catalog sends logs for service operations and job executions to Cloud Logging.”
• Cloud Logging works with VPC Service Controls From the official documentation: “This page describes how you can find VPC Service Controls errors using Cloud Logging.”
• Cloud Monitoring works with Knowledge Catalog From the official documentation: “Knowledge Catalog is integrated with Cloud Monitoring, which collects metrics, events, and metadata from Knowledge Catalog services.”
• Cloud Monitoring works with Sensitive Data Protection From the official documentation: “Sensitive Data Protection sends detailed telemetry about your Sensitive Data Protection usage to Monitoring.”
• Cloud Monitoring works with VPC Service Controls From the official documentation: “Cloud Monitoring performs the VPC perimeter check when a project is *added* to a metrics scope:”
• 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.”
• Data Catalog works with VPC Service Controls From the official documentation: “The Data Catalog integration with VPC Service Controls has no known limitations.”
• 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).”
• Knowledge Catalog works with VPC Service Controls From the official documentation: “The integration between Looker (Google Cloud core) and Knowledge Catalog supports VPC Service Controls.”
• ML.TFDV_VALIDATE is part of BigQuery From the official documentation: “Vertex AI Feature Store compares the snapshots from consecutive feature monitor job executions and calculates drifts using [the ML.TFDV_VALIDATE function in BigQuery](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-tfdv-validate).”
• online store is part of Agent Platform Feature Store From the official documentation: “In a typical workflow of a Feature Store setup, you first prepare your SAP data source in BigQuery, register your data sources by creating feature groups and features, and set up online store and feature view resources.”
• Optimized online serving is part of Agent Platform Feature Store From the official documentation: “Optimized online serving in Agent Platform Feature Store supports embedding management.”
• 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.”
• VPC Service Controls secures Cloud Logging From the official documentation: “The API for Cloud Logging can be protected by VPC Service Controls and the product can be used normally inside service perimeters.”