SageMaker Error – InvalidAction: The action or operation requested is invalid

#aws#sagemaker#machinelearning#cloud

A diagnostic guide for fixing API mismatches and the common "Control Plane vs. Data Plane" confusion.

Problem

You try to run a command—usually invoking an endpoint or checking status—and it fails immediately.

The Error:
An error occurred (InvalidAction) when calling the InvokeEndpoint operation: The action or operation requested is invalid. Verify that the action is typed correctly.

Potential causes:

• The "Two APIs" Confusion: You are trying to send a Runtime command (like InvokeEndpoint) to the Management API, or vice versa.
• VPC Endpoint Mismatch: You have a PrivateLink set up for the Training API, but you are trying to route Inference traffic through it.
• Typos: You manually typed the action name (e.g., CreateTrainingJobb) in a custom REST request or curl command.

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Clarifying the Issue

This error usually isn't about permissions (AccessDenied) or your payload (ValidationError). It is about routing.

SageMaker is unique because it is split into two distinct services:

1. The Control Plane (sagemaker.us-east-1.amazonaws.com): Handles "HR" tasks. Creating jobs, deploying models, listing resources.
2. The Data Plane (runtime.sagemaker.us-east-1.amazonaws.com): Handles "Work" tasks. Sending data to models and getting predictions (InvokeEndpoint).

The Analogy:
Imagine walking into the Corporate Headquarters (Control Plane) and asking the receptionist to drill a hole in a piece of metal. She will look at you confused and say, "We don't do that here; you need the Factory (Data Plane)."
That confusion is InvalidAction.

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Why It Matters

This error traps developers who are building custom integrations, switching from training to inference, or setting up private networks (VPC). You can waste hours debugging IAM roles when the problem is simply that you are knocking on the wrong door.

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Key Terms

• Control Plane: The management API used to configure resources (create endpoints, start training jobs).
• Data Plane (Runtime): The execution API used for real-time inference (invoking endpoints).
• Boto3 Client: The low-level AWS SDK for Python. You must instantiate separate clients (sagemaker vs sagemaker-runtime) for each plane.
• PrivateLink (VPC Endpoint): A private network interface that connects your VPC directly to AWS services without traversing the public internet.

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Common Scenarios Checklist

• ✅ Are you using Python/Boto3? → You likely initialized the wrong client type (Step 1).
• ✅ Are you using Postman or curl? → You are likely targeting the wrong URL subdomain (Step 2).
• ✅ Are you using VPC Endpoints? → You might be sending runtime traffic to the management interface (Step 3).

Steps at a Glance

1. Check your SDK Client Type (sagemaker vs. sagemaker-runtime).
2. Verify the URL Subdomain (Management vs. Runtime).
3. Audit VPC Endpoint Routes (The PrivateLink trap).

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Detailed Steps

Step 1: Check your SDK Client Type.

If you are using boto3, you cannot use one client for everything. You must initialize the specific client for the task you are doing.

Note: If you use the high-level sagemaker.predictor.Predictor class, the SDK handles this for you. This error usually happens when using the low-level boto3 client directly.

The Mistake:

# Initializing the Management client
client = boto3.client('sagemaker') 

# Trying to call a Runtime method
response = client.invoke_endpoint(...) 
# Result: AttributeError in Python (method doesn't exist on this client)
# Result: InvalidAction if passed through a proxy or generic HTTP client

The Fix:
Separate your concerns. Use the Runtime client for inference.

# For Training/Deploying
sm_client = boto3.client('sagemaker')

# For Inference
runtime_client = boto3.client('sagemaker-runtime')

response = runtime_client.invoke_endpoint(...)

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Step 2: Verify the URL Subdomain.

If you are manually constructing HTTP requests (Postman, Java, C++, curl), you must send the request to the correct server.

The Mistake:
Sending POST /endpoints/my-model/invocations to:
https://sagemaker.us-east-1.amazonaws.com ❌
(The Management API doesn't know what "invocations" are.)

The Fix:
Target the Runtime subdomain:
https://runtime.sagemaker.us-east-1.amazonaws.com ✅

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Step 3: Audit VPC Endpoint Routes.

If you are in a secure environment using AWS PrivateLink (VPC Endpoints), you must create two endpoints if you want to do both training and inference.

• Endpoint Service 1: com.amazonaws.us-east-1.sagemaker.api (Control Plane)
• Endpoint Service 2: com.amazonaws.us-east-1.sagemaker.runtime (Data Plane)

The Fix:
If you only created the .api endpoint, but your code tries to call invoke_endpoint(), the traffic has nowhere to go (or tries to go to the .api endpoint and gets rejected). Ensure you have provisioned the Runtime VPC Endpoint for your subnets.

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Pro Tips

The CLI Namespace
The AWS CLI also separates these services.

• Wrong: aws sagemaker invoke-endpoint ... (Will fail or suggest valid choices).
• Correct: aws sagemaker-runtime invoke-endpoint ...

"Unknown Operation"
Sometimes this error manifests as UnknownOperationException. Treat this exactly the same as InvalidAction. It means the specific API method you requested does not exist on the server you contacted.

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Conclusion

InvalidAction is almost always a case of mistaken identity. You are asking the Administrator to do the Factory Worker's job. By strictly separating your Management tasks (deploying, listing) from your Runtime tasks (predicting), and ensuring your URLs and Clients match that separation, you will eliminate this error entirely.

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Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.