SageMaker AI: Delete action from the CLI

Part 929 of AWS from Zero. This is lesson 16 in the SageMaker AI track.

What we are learning

Use delete-action to remove, stop, release, or detach one SageMaker AI resource in a dependency-aware way. This lesson identifies the required input shape, saves the raw response, and keeps inspection separate from execution.

The AWS CLI operation is aws sagemaker delete-action. Required operation inputs: --action-name (string). The modeled top-level response contains ActionArn.

Before you run it

aws sts get-caller-identity
REGION="${AWS_REGION:-ap-south-1}"
ACTION_NAME="replace-with-action-name"
aws sagemaker delete-action help

Use a sandbox account or an approved learning environment. Read the operation help before supplying identifiers, ARNs, network ranges, policy documents, or customer data.

Cost note: Notebook, training, processing, endpoint, pipeline, storage, and data services can incur charges.

The command

aws sagemaker delete-action \
  --action-name "$ACTION_NAME" \
  --region "$REGION" \
  --output json > part-929-response.json

The response is saved to part-929-response.json so inspection is separate from execution. The explicit variables above keep required identifiers visible before the API call.

Inspect the result

node -e "const r=require('./part-929-response.json'); console.log(Object.keys(r))"
node -e "const r=require('./part-929-response.json'); console.log(JSON.stringify(r, null, 2))"

Compare the returned identifiers and status fields with the account, Region, and resource you intended to target. For asynchronous operations, continue with the service's matching get, list, or describe command until it reaches a terminal state.

One tiny variation

node -e "const r=require('./part-929-response.json'); console.log(JSON.stringify(r["ActionArn"], null, 2))"

This variation changes output inspection rather than adding another infrastructure concept. Keep the raw JSON while developing a query so a narrow projection does not hide an error or unexpected field.

Common mistake

Deletion and stop operations can be irreversible or blocked by dependencies. Capture the resource identifier and current configuration before running the final command.

Cleanup

# This operation is read-only, operational, or needs resource-specific rollback.
# Re-read the command output before changing shared infrastructure.
rm -f part-929-request.json part-929-response.json part-929-payload.bin part-929-debug.log

Local request and response files may contain account IDs, ARNs, names, or service configuration. Remove them when the lab is complete and follow dependency-aware cleanup for any AWS resource you created.

Next, we will learn SageMaker AI: Delete algorithm from the CLI.

Official AWS CLI reference