Model Registry

Related topics

• BigQuery ML works with Model Registry From the official documentation: “Automatically register a BigQuery ML model to Model Registry”
• 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.”
• 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.”
• endpoint works with Model Registry From the official documentation: “From the Model Registry, you can deploy your imported model to an endpoint and run inferences.”
• model is part of Model Registry From the official documentation: “This guide describes how to import models into the Gemini Enterprise Agent Platform Model Registry.”
• model version is part of Model Registry From the official documentation: “With Model Registry you can view your models and all of their versions in a single view.”
• 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: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
• 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 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.”
• 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.”
• Cloud Storage works with customer-managed encryption keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
• 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 Gemini Enterprise Agent Platform From the official documentation: “When you perform custom training, Agent Platform usually loads your data from Cloud Storage.”
• 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.”
• remote model is part of BigQuery ML From the official documentation: “- Create a BigQuery ML remote model.”
• Agent Platform works with BigQuery ML From the official documentation: “Learn how to use BigQuery ML for training with Gemini Enterprise Agent Platform.”
• 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:”
• 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 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.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• 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 depends on Cloud Storage From the official documentation: “If used for storing Customer-managed Encryption Keys (CMEK): Cloud Storage (See [Cloud Storage pricing](/storage/pricing).)”
• endpoint works with model From the official documentation: “Deploy the model resource to a serving Endpoint resource.”
• remote model depends on Gemini Enterprise Agent Platform From the official documentation: “Create a remote model that represents a hosted Agent Platform model:”
• 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 Colab Enterprise From the official documentation: “You can create a model tuning job in Agent Platform by using the side panel in Colab Enterprise.”
• 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 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 remote model From the official documentation: “The function uses a BigQuery ML [remote model](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-service) to connect BigQuery to the Cloud Natural Language API.”
• BigQuery ML is part of BigQuery From the official documentation: “BigQuery ML models are stored in BigQuery datasets, similar to tables and views.”
• BigQuery ML depends on Cloud Storage From the official documentation: “BigQuery ML imports the model from Cloud Storage by using the credentials of the user who runs the `CREATE MODEL` statement.”
• BigQuery ML works with Colab Enterprise From the official documentation: “Integrated [Colab Enterprise notebooks in BigQuery](/bigquery/docs/programmatic-analysis)”
• BigQuery ML is an alternative to Gemini Enterprise Agent Platform From the official documentation: “You can use [BigQuery ML](/bigquery/docs/bqml-introduction) or [Agent Platform development tools](/gemini-enterprise-agent-platform/machine-learning/general/developer-tools-overview) to develop, train, and evaluate ML models.”
• Colab Enterprise works with customer-managed encryption keys From the official documentation: “For more information about how to use CMEK for Colab Enterprise, see the [Colab Enterprise CMEK page](/colab/docs/cmek).”
• Colab Enterprise is part of Gemini Enterprise Agent Platform From the official documentation: “Colab Enterprise is a part of Gemini Enterprise Agent Platform.”
• customer-managed encryption keys is part of BigQuery From the official documentation: “If you require more control over encryption keys, BigQuery supports [customer-managed encryption keys](/bigquery/docs/customer-managed-encryption) in the [Cloud Key Management Service](/kms/docs).”
• 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.”
• customer-managed encryption keys secures endpoint From the official documentation: “For **endpoints**, all model files used for the model deployment under the endpoint are CMEK-encrypted.”
• customer-managed encryption keys secures model From the official documentation: “For **models**, the models stored in the storage system (for example, disk) are CMEK-encrypted.”
• 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 remote model From the official documentation: “Create a remote model that represents a hosted Agent Platform `gemini-3.5-flash-lite` model:”
• model depends on endpoint From the official documentation: “You must deploy the model to an endpoint before the model can be used to serve online inferences.”
• model is part of Gemini Enterprise Agent Platform From the official documentation: “`Model`, this resource is available in Gemini Enterprise Agent Platform.”