cloudml-hypertune

Related topics

• Agent Platform works with cloudml-hypertune From the official documentation: “Use the [`cloudml-hypertune` Python package](https://github.com/GoogleCloudPlatform/cloudml-hypertune) to pass metrics to Agent Platform.”
• cloudml-hypertune works with Gemini Enterprise Agent Platform From the official documentation: “Your training code must intermittently report the hyperparameter metric that you are trying to optimize to Agent Platform. For example, if you want to maximize your model's accuracy, you might want to report this metric at the end of every training epoch. Agent Platform uses…”
• VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
• 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 BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
• 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.”
• 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.”
• 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).”
• 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.”
• 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 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 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).”
• 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…”
• AutoML is part of Agent Platform From the official documentation: “Train a model. You can do this in Gemini Enterprise Agent Platform using AutoML or custom training.”
• 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.”
• 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.”
• customer-managed encryption keys is part of Agent Platform From the official documentation: “See Agent Engine [Customer-managed encryption keys (CMEK)](/gemini-enterprise-agent-platform/scale/runtime/manage-agent-access#cmek) for more details.”
• 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.”
• VPC Service Controls secures Agent Platform From the official documentation: “(Optional) To mitigate the risk of data exfiltration from Agent Platform, enable VPC Service Controls and specify a VPC network when you create a cluster.”
• VPC Service Controls secures Model Armor From the official documentation: “The API for Model Armor can be protected by VPC Service Controls and the product can be used normally inside service perimeters.”