prebuilt containers

Related topics

• prebuilt containers is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform provides Docker container images that you run as *prebuilt containers* for serverless training.”
• custom container is an alternative to prebuilt containers From the official documentation: “You can choose to run your training code with one of these containers, or create a custom container that has your training code and dependencies pre-installed.”
• prebuilt containers is part of Agent Platform From the official documentation: “Agent Platform provides prebuilt containers with the following machine learning (ML) frameworks: PyTorch, scikit-learn, TensorFlow, and XGBoost.”
• 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”
• 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.”
• 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.”
• 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.”
• 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.”
• 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).”
• 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 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.”
• Cloud Storage works with custom container From the official documentation: “The container can write results to a file named `results.json` (in JSON format), located in the Cloud Storage bucket provided by Cloud Deploy.”
• 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.”
• 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.”