VPC Network Peering

Related topics

• Private Service Connect is an alternative to VPC Network Peering From the official documentation: “Use VPC Network Peering if you need to insert NVAs or if your application doesn't support Private Service Connect”
• Network Connectivity Center is an alternative to VPC Network Peering From the official documentation: “This document presents three architectural options for setting up a hub-and-spoke network topology in Google Cloud. The first option uses Network Connectivity Center, the second uses VPC Network Peering, and the third uses Cloud VPN.”
• Compute Engine works with VPC Network Peering From the official documentation: “VPC Network Peering works with [Compute Engine](/compute/docs), [GKE](/kubernetes-engine/docs), and [App Engine flexible environment](/appengine/docs/flexible).”
• CustomJob works with VPC Network Peering From the official documentation: “Optional settings for configuring job scheduling ([`Scheduling`](/gemini-enterprise-agent-platform/reference/rest/v1/CustomJobSpec#scheduling)), [setting certain environment variables for your training…”
• Gemini Enterprise Agent Platform works with VPC Network Peering From the official documentation: “This guide shows how to set up [VPC Network Peering](/vpc/docs/vpc-peering) to peer your network with Agent Platform resources.”
• GKE works with VPC Network Peering From the official documentation: “VPC Network Peering works with [Compute Engine](/compute/docs), [GKE](/kubernetes-engine/docs), and [App Engine flexible environment](/appengine/docs/flexible).”
• VPC Network Peering configures Private Endpoints From the official documentation: “You can configure Vertex AI private endpoints by using VPC Network Peering.”
• VPC Network Peering is part of Virtual Private Cloud From the official documentation: “VPC includes VPC Network Peering, Private Service Connect, private services access, and Shared VPC.”
• 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 works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
• VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
• Cloud Logging works with GKE From the official documentation: “Enable Cloud Logging on GKE Clusters”
• Cloud Monitoring works with GKE From the official documentation: “Enable Cloud Monitoring on GKE Clusters”
• Compute Engine is an alternative to GKE From the official documentation: “You can also deploy a third-party model to either Vertex AI Model Garden or self-host on GKE or Compute Engine.”
• 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 Logging works with Cloud Monitoring From the official documentation: “You can store and view query-response activity logs in Cloud Logging and set up logs-based monitoring by using Cloud Monitoring.”
• 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 Storage works with Compute Engine From the official documentation: “For example, you can create a service account that your Compute Engine instances use to access objects stored in Cloud Storage buckets.”
• VPC Service Controls secures Gemini Enterprise Agent Platform From the official documentation: “[VPC Service Controls](/vpc-service-controls/docs/overview) can help you mitigate the risk of data exfiltration from Gemini Enterprise Agent Platform.”
• BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
• Cloud Logging works with Compute Engine From the official documentation: “Compute Engine sends you Cloud Logging events for scheduled, started, or completed maintenance.”
• Cloud Logging works with Gemini Enterprise Agent Platform From the official documentation: “When you run a training job, Gemini Enterprise Agent Platform training saves logs to [Logging](/logging/docs).”
• 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.”
• GKE depends on Compute Engine From the official documentation: “For example, GKE deploys clusters on Compute Engine VMs.”
• VPC Service Controls secures Cloud Storage From the official documentation: “Storage Transfer Service supports transfers to Cloud Storage buckets protected by VPC Service Controls.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Private Service Connect From the official documentation: “Click the private zone that contains the DNS records for the Private Service Connect endpoints, which you created to allow BigQuery Toolkit for SAP to privately connect to the BigQuery and IAM APIs.”
• Cloud Logging is an alternative to Cloud Storage From the official documentation: “Instead, they are routed to either Cloud Storage (recommended) or Cloud Logging, depending on your organization's configuration.”
• Gemini Enterprise Agent Platform works with Private Service Connect From the official documentation: “Use Private Service Connect to access Agent Platform from on-premises”
• Network Connectivity Center works with Private Service Connect From the official documentation: “Private Service Connect consumer access points are reachable over Network Connectivity Center.”
• Private Service Connect works with VPC Service Controls From the official documentation: “VPC Service Controls that use Private Service Connect”
• VPC Service Controls secures Compute Engine From the official documentation: “To further protect your Compute Engine resources, you can protect them using [VPC Service Controls](/vpc-service-controls/docs/overview).”
• 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 Compute Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
• BigQuery works with GKE From the official documentation: “Subsequently, by integrating BigQuery with GKE, you can use BigQuery to efficiently handle massive datasets (like Google Analytics) and the model's natural language understanding capabilities to generate meaningful insights.”
• 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.”
• 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 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 Compute Engine From the official documentation: “Many Google services like Compute Engine send telemetry to Cloud Monitoring.”
• Cloud Monitoring works with Private Endpoints From the official documentation: “To customize monitoring, query the following metrics in Cloud Monitoring: - `aiplatform.googleapis.com/prediction/online/private/response_count`”
• Cloud Monitoring works with Private Service Connect From the official documentation: “Private Service Connect exposes key metrics to Cloud Monitoring that give you insights into your Private Service Connect connections.”
• 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 GKE From the official documentation: “This example configures GKE to store Pod snapshots in the `FOLDER_PATH/` path inside the Cloud Storage bucket `BUCKET_NAME`:”
• Cloud Storage works with Vertex AI From the official documentation: “Access Cloud Storage content through your current project's Vertex AI service agent.”
• Compute Engine works with Gemini Enterprise Agent Platform From the official documentation: “Google Compute Engine provides a runtime environment for all features provided by Managed Service for Apache Spark and Gemini Enterprise Agent Platform.”
• Compute Engine works with Virtual Private Cloud From the official documentation: “Google Cloud Virtual Private Cloud (VPC) provides networking functionality to Compute Engine virtual machine (VM) instances, Google Kubernetes Engine (GKE) containers, and serverless workloads.”
• CustomJob is part of Gemini Enterprise Agent Platform From the official documentation: “Serverless training jobs (`CustomJob` resources in the Agent Platform API) are the basic way to run your custom machine learning (ML) training code in Gemini Enterprise Agent Platform.”
• CustomJob is part of Vertex AI From the official documentation: “A CustomJob is one of three Vertex AI resources a user can create to train custom models on Vertex AI.”
• 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 is an alternative to GKE From the official documentation: “You can deploy supported Hugging Face models in Gemini Enterprise Agent Platform or Google Kubernetes Engine (GKE).”
• GKE works with Private Service Connect From the official documentation: “This page provides an overview of Private Service Connect in Google Kubernetes Engine (GKE) clusters.”
• GKE is an alternative to Vertex AI From the official documentation: “Once you have your fine-tuned model, you can deploy it to a serving endpoint using Google Kubernetes Engine (GKE) or Vertex AI to make it accessible for inference.”
• GKE depends on Virtual Private Cloud From the official documentation: “Google Kubernetes Engine (GKE) networking uses and extends the software-defined networking (SDN) infrastructure provided by Virtual Private Cloud (VPC).”
• GKE works with Virtual Private Cloud From the official documentation: “The following example creates a GKE cluster and a Virtual Private Cloud (VPC) network that supports GKE.”
• Network Connectivity Center works with Virtual Private Cloud From the official documentation: “NCC lets you connect multiple Virtual Private Cloud (VPC) networks and on-premises locations.”
• Private Service Connect works with Vertex AI From the official documentation: “Private services access and Private Service Connect are only supported with Vertex AI integration.”
• Private Service Connect is part of Virtual Private Cloud From the official documentation: “VPC includes VPC Network Peering, Private Service Connect, private services access, and Shared VPC.”
• Private Service Connect works with Virtual Private Cloud From the official documentation: “[Private Service Connect](https://cloud.google.com/private-service-connect) | Create a private and secure connection from your VPCs to Google, third parties, or your own services.”