Update the Approval Status of a Model
After you create a model version, you typically want to evaluate its performance
before you deploy it to a production endpoint. If it performs to your requirements,
you can update the approval status of the model version to Approved
.
Setting the status to Approved
can initiate CI/CD deployment for the
model. If the model version does not perform to your requirements, you can update
the approval status to Rejected
.
You can manually update the approval status of a model version after you register it, or you can create a condition step to evaluate the model when you create a SageMaker AI pipeline. For information about creating a condition step in a SageMaker AI pipeline, see Pipelines steps.
When you use one of the SageMaker AI provided project templates and the approval status of a model version changes, the following action occurs. Only valid transitions are shown.
-
PendingManualApproval
toApproved
– initiates CI/CD deployment for the approved model version -
PendingManualApproval
toRejected
– No action -
Rejected
toApproved
– initiates CI/CD deployment for the approved model version -
Approved
toRejected
– initiates CI/CD to deploy the latest model version with anApproved
status
You can update the approval status of a model version by using the AWS SDK for Python (Boto3) or by using the Amazon SageMaker Studio console. You can also update the approval status of a model version as part of a condition step in a SageMaker AI pipeline. For information about using a model approval step in a SageMaker AI pipeline, see Pipelines overview.
Update the Approval Status of a Model (Boto3)
When you created the model version in Register a Model Version, you set the
ModelApprovalStatus
to PendingManualApproval
. You
update the approval status for the model by calling
update_model_package
. Note that you can automate this process
by writing code that, for example, sets the approval status of a model depending
on the result of an evaluation of some measure of the model's performance. You
can also create a step in a pipeline that automatically deploys a new model
version when it is approved. The following code snippet shows how to manually
change the approval status to Approved
.
model_package_update_input_dict = { "ModelPackageArn" : model_package_arn, "ModelApprovalStatus" : "Approved" } model_package_update_response = sm_client.update_model_package(**model_package_update_input_dict)
Update the Approval Status of a Model (Studio or Studio Classic)
To manually change the approval status in the Amazon SageMaker Studio console, complete the following steps based on whether you use Studio or Studio Classic.