Vertex Explainable AI

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• 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).”
• 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).”
• BigQuery ML works with Vertex Explainable AI From the official documentation: “This explainability feature is integrated with [BigQuery ML](/bigquery/docs/xai-overview) and [Vertex Explainable AI](/vertex-ai/docs/explainable-ai/overview), which provide feature-based explanations.”
• 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”
• 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.”
• 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 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 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.”
• 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.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• 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.”
• 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.”
• 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:”
• 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.”
• 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.”
• 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 BigQuery ML From the official documentation: “BigQuery ML integrates with Agent Platform, which is the end-to-end platform for AI and ML in Google Cloud.”
• Agent Platform works with Model Registry From the official documentation: “Exporting your models is not required for deployment on Gemini Enterprise Agent Platform if you are using the Vertex AI Model Registry.”
• 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 BigQuery ML From the official documentation: “If your tabular data is stored in BigQuery ML, you can train an AutoML tabular model directly in BigQuery ML.”
• 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 BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
• 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 Gemini Enterprise Agent Platform Pipelines From the official documentation: “To learn more about using Vertex AI Pipelines with BigQuery, see [Export and deploy a BigQuery machine learning model for prediction](https://codelabs.developers.google.com/codelabs/bqml-vertex-prediction#0).”
• BigQuery ML works with Gemini Enterprise Agent Platform Pipelines From the official documentation: “Training an acquisition-prediction model using Swivel, BigQuery ML and Gemini Enterprise Agent Platform Pipelines”
• Cloud Storage works with Gemini Enterprise Agent Platform Pipelines From the official documentation: “Agent Platform Pipelines uses Cloud Storage to store the artifacts of your pipeline runs.”
• 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 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 Pipelines is part of Agent Platform From the official documentation: “Agent Platform provides a managed service which implements the Kubeflow SDK: Gemini Enterprise Agent Platform Pipelines.”
• Gemini Enterprise Agent Platform Pipelines is part of Gemini Enterprise Agent Platform From the official documentation: “You can configure Private Service Connect interface connections for certain resources in Gemini Enterprise Agent Platform, including: - [Ray on Vertex AI](/gemini-enterprise-agent-platform/machine-learning/open-source/ray/create-cluster#enable_interface) - [Custom…”