Gemini Enterprise Agent Platform

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• 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.”
• customer-managed encryption keys secures Gemini Enterprise Agent Platform From the official documentation: “Enable customer-managed encryption keys (CMEK) for the Gemini Enterprise Agent Platform to gain more control over data encryption and key management.”
• 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.”
• AutoML is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform offers two model training methods, AutoML and custom training.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• Gemini Enterprise Agent Platform works with Vertex Explainable AI From the official documentation: “Feature attributions are included in Agent Platform inferences through [Vertex Explainable AI](/gemini-enterprise-agent-platform/machine-learning/tabular-data/classification-explanations).”
• Model Registry is part of Gemini Enterprise Agent Platform From the official documentation: “Import a pretrained TensorFlow model into the Gemini Enterprise Agent Platform Model Registry.”
• Colab Enterprise is part of Gemini Enterprise Agent Platform From the official documentation: “Colab Enterprise is a part of Gemini Enterprise Agent Platform.”
• 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.”
• Model Garden is part of Gemini Enterprise Agent Platform From the official documentation: “The following open models are offered as managed APIs on Gemini Enterprise Agent Platform Model Garden (MaaS):”
• 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.”
• 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.”
• 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.”
• AutoML works with Gemini Enterprise Agent Platform From the official documentation: “Learn how to use `AutoML` for training with `Gemini Enterprise Agent Platform`.”
• AutoML is an alternative to Gemini Enterprise Agent Platform From the official documentation: “Perform serverless training on Gemini Enterprise Agent Platform to run your own machine learning (ML) training code in the cloud, instead of using AutoML.”
• Cloud KMS works with Gemini Enterprise Agent Platform From the official documentation: “Confirm that the original Cloud KMS key is still enabled and that the Gemini Enterprise Agent Platform service agent still has access.”
• Colab Enterprise works with Gemini Enterprise Agent Platform From the official documentation: “To learn more about using the side panel with your Agent Platform tuning jobs, see [Interact with Agent Platform to tune a model](/colab/docs/tuning).”
• 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 Google Cloud Storage From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your AutoML tabular model to Cloud Storage, download the model to an on-premises server or a server hosted by another cloud provider, and then use Docker to make the model available for predictions.”
• 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 Model Garden From the official documentation: “Model Garden provides built-in integration with other parts of Gemini Enterprise Agent Platform such as model tuning, evaluation, and serving”
• 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).”
• Vertex Explainable AI depends on Gemini Enterprise Agent Platform From the official documentation: “Vertex Explainable AI isn't available using exported tabular models. If you need to use Vertex Explainable AI, you must serve predictions from a model hosted by Agent Platform.”
• 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”
• Model Armor works with Vertex AI From the official documentation: “The following example enables Model Armor on a Vertex AI endpoint.”
• BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
• VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
• customer-managed encryption keys depends on Cloud KMS From the official documentation: “You must have a symmetric encryption key in Cloud KMS to use for CMEK.”
• BigQuery is an alternative to Google Cloud Storage From the official documentation: “The training data can be either a CSV file in Cloud Storage or a table in BigQuery.”
• Cloud Run functions works with Cloud Storage From the official documentation: “Set up functions in response to events in Cloud Storage:”
• BigQuery works with Cloud Run functions From the official documentation: “BigQuery supports external function calls through Cloud Run functions.”
• BigQuery works with Google Cloud Storage From the official documentation: “Load Cloud Storage data into BigQuery”
• 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 Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
• customer-managed encryption keys secures Cloud Storage From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
• BigQuery ML works with Model Registry From the official documentation: “Automatically register a BigQuery ML model to Model Registry”
• customer-managed encryption keys secures BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
• Cloud Storage works with customer-managed encryption keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• 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.”
• BigQuery works with Colab Enterprise From the official documentation: “You can use Colab Enterprise from within the Google Cloud console, with features built into both Agent Platform and BigQuery.”
• Cloud KMS works with customer-managed encryption keys From the official documentation: “With customer-managed encryption keys (CMEK), volume keys are wrapped using your keys stored in Cloud KMS.”
• 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 works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• 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.”
• Cloud Run functions works with Google Cloud Storage From the official documentation: “Service account for Dataminr Alerts Cloud Run function to write alert data to GCS”
• customer-managed encryption keys is part of Cloud KMS From the official documentation: “To protect your agents' data by using encryption keys that you control,<br>you can use [CMEKs](/kms/docs/cmek)<br>that you create and manage in Cloud KMS.”
• 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.”
• 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 KMS From the official documentation: “# Enable the BigQuery service account to encrypt/decrypt Cloud KMS keys”
• 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 Run functions depends on Cloud Storage From the official documentation: “Cloud Run functions uses the config in Cloud Storage to determine what actions to take for each stateful MIG.”
• Cloud Storage works with Colab Enterprise From the official documentation: “Colab Enterprise stores the results in Cloud Storage for you to review and share with others.”
• customer-managed encryption keys is part of Cloud Storage From the official documentation: “[Cloud Storage](/storage/docs/introduction) provides features such as customer-managed encryption, lifecycle management, and aging and retention policies.”
• customer-managed encryption keys secures Colab Enterprise From the official documentation: “This guide describes how to use CMEK for Colab Enterprise.”