Update Project

sagemaker_update_project

R Documentation

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model¶

Description¶

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Usage¶

sagemaker_update_project(ProjectName, ProjectDescription,
  ServiceCatalogProvisioningUpdateDetails, Tags)

Arguments¶

ProjectName: [required] The name of the project.
ProjectDescription: The description for the project.
ServiceCatalogProvisioningUpdateDetails: The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
Tags: An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. In addition, the project must have tag update constraints set in order to include this parameter in the request. For more information, see Amazon Web Services Service Catalog Tag Update Constraints.

Value¶

A list with the following syntax:

list(
  ProjectArn = "string"
)

Request syntax¶

svc$update_project(
  ProjectName = "string",
  ProjectDescription = "string",
  ServiceCatalogProvisioningUpdateDetails = list(
    ProvisioningArtifactId = "string",
    ProvisioningParameters = list(
      list(
        Key = "string",
        Value = "string"
      )
    )
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)