Vertex AI

Related topics

• Model Armor works with Vertex AI From the official documentation: “The following example enables Model Armor on a Vertex AI endpoint.”
• Google Gen AI SDK works with Vertex AI From the official documentation: “Set environment variables to use the Google Gen AI SDK with Vertex AI:”
• Vertex AI is part of Gemini Enterprise Agent Platform From the official documentation: “Vertex AI's services are now part of Gemini Enterprise Agent Platform.”
• Cloud Run works with Vertex AI From the official documentation: “The Cloud Run service can interact with Vertex AI services.”
• Identity and Access Management secures Vertex AI From the official documentation: “A Vertex AI model with an active endpoint that you have Identity and Access Management (IAM) permission to access.”
• Model Armor secures Vertex AI From the official documentation: “The following example enables Model Armor on a Vertex AI endpoint.”
• 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).”
• feature serving is part of Vertex AI From the official documentation: “In Vertex AI, there are two types of feature serving—online serving and offline serving.”
• Gemini is part of Vertex AI From the official documentation: “Google Cloud provides a set of state-of-the-art foundation models through Vertex AI, including Gemini.”
• Google Kubernetes Engine is an alternative to Vertex AI From the official documentation: “| **Small ML Models** | Requires specific hardware (e.g., GPU). | Vertex AI / Google Kubernetes Engine (GKE) | Specialized hardware; more complex setup. |”
• Online Prediction is part of Vertex AI From the official documentation: “Vertex AI offers online predictions on Google Distributed Cloud (GDC) air-gapped through the Online Prediction API.”
• vector search works with Vertex AI From the official documentation: “Reference architecture for a RAG-capable generative AI application using Vertex AI and Vector Search.”
• vector search is part of Vertex AI From the official documentation: “Vertex AI features for AutoML Forecasting, AutoML Tabular, Batch Prediction, Online Prediction, Pipelines, Training, Vector Search, and Vizier are available in [10 additional Google Cloud regions](/vertex-ai/docs/general/locations#feature-availability).”
• Vertex AI works with Vertex AI Workbench From the official documentation: “Vertex AI Workbench integrations and features make accessing your machine learning data easier, sharing and processing data faster, interacting with Vertex AI services using the Python programming language, and more.”
• Vertex AI Workbench is part of Vertex AI From the official documentation: “The API endpoint for the Vertex AI Workbench KRM API is: `https://ENDPOINT/apis/aiplatform.gdc.goog/v1`”
• 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 Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
• Cloud Run works with Cloud Storage From the official documentation: “Route Cloud Storage events to Cloud Run”
• 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 Cloud Audit Logs From the official documentation: “Cloud Audit Logs maintain a record of Google Cloud events, including BigQuery events.”
• 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 Audit Logs works with VPC Service Controls From the official documentation: “VPC Service Controls integrate with Cloud Audit Logs to monitor access to the control plane.”
• 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 Gemini From the official documentation: “Use Gemini to analyze your enterprise data in BigQuery.”
• BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• Cloud Audit Logs works with Cloud Storage From the official documentation: “Use Cloud Audit Logs to generate logs for API operations performed in Cloud Storage.”
• Cloud Logging works with Model Armor From the official documentation: “Use Cloud Logging to analyze Model Armor usage patterns, errors, and performance metrics.”
• Gemini Enterprise Agent Platform works with Model Armor From the official documentation: “If you use floor settings for the [Agent Platform integration](/model-armor/model-armor-vertex-integration), all Model Armor features are available in the following locations.”
• 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.”
• Agent Platform works with Cloud Storage From the official documentation: “Load the model's `state_dict` object into Cloud Storage by using a Agent Platform custom job:”
• BigQuery works with Cloud Run From the official documentation: “You can then use Cloud Run to transcode data present in the bucket and move it to BigQuery.”
• 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 Logging works with Cloud Run From the official documentation: “Cloud Logging captures, stores, and analyzes Cloud Run logs.”
• Cloud Logging works with Google Kubernetes Engine From the official documentation: “As an example, if you are using GKE, you can [use Cloud Logging to query](/logging/docs/view/building-queries) for GKE cluster audit logs.”
• Cloud Monitoring works with Cloud Run From the official documentation: “Cloud Monitoring records metrics regarding the execution of Cloud Run.”
• Cloud Monitoring works with Model Armor From the official documentation: “This graph uses the metrics that Model Armor publishes to Cloud Monitoring.”
• Cloud Run works with Gemini From the official documentation: “Learn how to build and deploy a LangChain application using Cloud Run and Gemini to respond to queries about city capitals.”
• Cloud Run works with Google Kubernetes Engine From the official documentation: “Improved security: take advantage of automatic IAM JWT injection for more secure communication between GKE and Cloud Run.”
• Cloud Run works with Identity and Access Management From the official documentation: “Cloud Run also offers the ability to control access to a service with Identity and Access Management (IAM).”
• 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.”
• Gemini Enterprise Agent Platform works with Google Gen AI SDK From the official documentation: “Set environment variables to use the Google Gen AI SDK with Gemini Enterprise Agent Platform”
• Google Kubernetes Engine depends on BigQuery From the official documentation: “This happens because Google Kubernetes Engine depends on BigQuery and Managed Service for Apache Airflow inherits this dependency from the Kubernetes Engine API.”
• 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 works with VPC Service Controls From the official documentation: “VPC Service Controls perimeters and Identity and Access Management (IAM) roles work together.”
• Model Armor secures Gemini Enterprise Agent Platform From the official documentation: “This document describes how to configure Model Armor to protect Gemini models on Gemini Enterprise Agent Platform by screening prompts and responses.”
• Agent Platform works with BigQuery From the official documentation: “BigQuery and Agent Platform are tightly integrated, letting you access and analyze your data directly within the Agent Platform platform.”
• Agent Platform works with Cloud Logging From the official documentation: “If any containers restart before Agent Platform shuts down the entire job, these containers may produce logs in Cloud Logging.”
• Agent Platform works with Gemini From the official documentation: “Gemini on Agent Platform can handle large volumes of content, making it suitable for platforms of all sizes.”
• Agent Platform works with Google Gen AI SDK From the official documentation: “Learn the basics of using the Google Gen AI SDK to send queries to Gemini models with Agent Platform.”
• Agent Platform works with Model Armor From the official documentation: “Model Armor integrates with Agent Platform and Google Cloud MCP servers to screen requests and responses from Gemini models and Google Cloud remote MCP servers based on the floor settings you define.”
• Agent Platform works with VPC Service Controls From the official documentation: “If you use Agent Platform in a VPC-SC environment, you must create an ingress rule in your perimeter to allow ingress from the Reasoning Engine Service Agent (`service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com`) into the `storage.googleapis.com` service and…”
• 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 Vertex AI Workbench From the official documentation: “Vertex AI Workbench provides a managed and scalable infrastructure with built-in integrations to other Google Cloud services like BigQuery and Cloud Storage, enabling data scientists to perform their machine learning tasks efficiently without managing underlying infrastructure.”