Agent Platform Feature Store

Related topics

• 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.”
• Agent Platform Feature Store is part of Gemini Enterprise Agent Platform From the official documentation: “In the Agent Platform section of the Google Cloud console, go to the **Feature Store** page.”
• 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.”
• 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.”
• feature view is part of Agent Platform Feature Store From the official documentation: “Learn how to use a dedicated service agent for a feature view in Agent Platform Feature Store.”
• 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.”
• 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.”
• feature view is part of online store From the official documentation: “The name of the online store containing the feature view.”
• BigQuery works with Vertex AI Feature Store From the official documentation: “In the new Vertex AI Feature Store, BigQuery serves as the offline store.”
• 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 Identity and Access Management From the official documentation: “To learn how to use IAM with Cloud Storage, see [Using IAM Permissions](/storage/docs/access-control/using-iam-permissions).”
• 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.”
• Identity and Access Management secures BigQuery From the official documentation: “Access to BigQuery data from AlloyDB is managed through IAM.”
• Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with feature view From the official documentation: “A BigQuery table or view directly associated with the feature view.”
• BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• VPC Service Controls secures Gemini Enterprise Agent Platform From the official documentation: “The API for Agent Platform can be protected by VPC Service Controls and the product can be used normally inside service perimeters.”
• 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.”
• 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.”
• Identity and Access Management secures Gemini Enterprise Agent Platform From the official documentation: “Agent Platform uses IAM to manage access to resources.”
• Identity and Access Management secures Knowledge Catalog From the official documentation: “IAM controls access to your Knowledge Catalog resources at the Google Cloud resource level.”
• Identity and Access Management works with VPC Service Controls From the official documentation: “VPC Service Controls perimeters and Identity and Access Management (IAM) roles work together.”
• 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)”
• 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.”
• 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 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 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 Gemini Enterprise Agent Platform From the official documentation: “This page provides information about how to use Cloud Logging to view details about a Gemini Enterprise Agent Platform model.”
• 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 Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform exports metrics to [Cloud Monitoring](/monitoring/docs).”
• Cloud Monitoring depends on Identity and Access Management From the official documentation: “To use Monitoring, you must have the appropriate [Identity and Access Management](/iam/docs/overview) (IAM) permissions.”
• 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 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).”
• 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 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).”
• feature view depends on BigQuery From the official documentation: “A feature view is a logical collection of features materialized from a BigQuery data source to an online store instance.”
• feature view works with online store From the official documentation: “To connect the BigQuery table, which stores the feature data source, to the `FeatureOnlineStore` instance, call the `CreateFeatureView` API to create a `FeatureView` resource.”
• 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 depends on Identity and Access Management From the official documentation: “Agent Platform uses IAM to manage access to resources.”
• Gemini Enterprise Agent Platform works with Identity and Access Management From the official documentation: “Learn more about [Gemini Enterprise Agent Platform access control with IAM](/gemini-enterprise-agent-platform/machine-learning/general/access-control).”
• 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.”
• Gemini Enterprise Agent Platform works with VPC Service Controls From the official documentation: “For information on using Gemini Enterprise Agent Platform VPC Service Controls, see [VPC Service Controls with Gemini Enterprise Agent Platform](/gemini-enterprise-agent-platform/machine-learning/general/vpc-service-controls).”
• 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 depends on Identity and Access Management From the official documentation: “Knowledge Catalog uses Identity and Access Management (IAM) for authorization.”
• 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.”
• 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.”
• 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.”