ML Model Cost Attribution with AWS CUR

Question

Pulumi · Accepted Answer

Cost attribution in cloud environments is an essential task for organizations that need to understand and monitor the usage and costs associated with their machine learning (ML) models. AWS provides the Cost and Usage Report (CUR) service, which delivers comprehensive data about your AWS costs and usage. CUR files are delivered to an Amazon S3 bucket and can be queried to analyze costs.

For attributing costs to ML models, you can utilize AWS Cost Explorer and Cost Allocation Tags. Cost Allocation Tags allow you to categorize and track your AWS costs. When applied to an AWS resource, such as Amazon SageMaker (which is used for ML models), these tags can be used in the CUR to organize and track costs.

You can define tags that correspond to your ML models and then activate those tags within the AWS Billing and Cost Management dashboard. Once activated, AWS starts adding the tag information into your CUR data after 24 hours, allowing for cost analysis based on those tags.

Below is a Pulumi program written in Python to demonstrate setting up a machine learning model and enabling cost allocation tags on AWS. We'll use the Amazon SageMaker service for the machine learning model and the AWS Cost Explorer service for the cost allocation tags.

```python
import pulumi
import pulumi_aws as aws

# First, define an Amazon SageMaker notebook instance which can be used
# for Jupyter notebooks that are often utilized for ML model development.
sagemaker_notebook_instance = aws.sagemaker.NotebookInstance("mySagemakerNotebookInstance",
    instance_type="ml.t2.medium")

# Define tags that can be used for cost allocation purposes.
# These tags will help attribute costs to specific ML models or related services.
cost_allocation_tags = {
    "Project": "ML-Model",
    "Environment": "Development"
}

# Apply the tags to the SageMaker notebook instance.
# Each resource you wish to track will need the tags applied directly.
sagemaker_notebook_instance_with_tags = aws.sagemaker.NotebookInstance("mySagemakerNotebookInstanceWithTags",
    instance_type="ml.t2.medium",
    tags=cost_allocation_tags)

# You would need to activate these tags in the AWS Billing and Cost Management dashboard.
# This instructs AWS to include the specific tags in CUR.

# Output the ARN (Amazon Resource Name) of the SageMaker notebook instance which
# could be further used in linking with AWS Cost Management tools.
pulumi.export("sagemaker_notebook_instance_arn", sagemaker_notebook_instance.arn)
pulumi.export("sagemaker_notebook_instance_with_tags_arn", sagemaker_notebook_instance_with_tags.arn)
```

In the program, we start by importing the required Pulumi AWS package. Then, we create an Amazon SageMaker Notebook Instance, which is a managed service that provides Jupyter notebooks for data exploration and model development purposes in machine learning workflows.

Next, we define a dictionary of cost allocation tags. These tags consist of key-value pairs that help you organize your AWS resources. In this example, we have used `Project` and `Environment` as keys and given them values that could identify the resources involved in a particular project or environment.

After defining the tags, we apply these tags to the SageMaker Notebook Instance. It's important to note that these tags need to be explicitly applied to each AWS resource you want to track for cost allocation.

Lastly, the program exports the Amazon Resource Name (ARN) of the SageMaker Notebook Instance resources, which can be used for further automation or integration with other services.

Please note, to fully enable and use cost allocation tags with the Cost and Usage Report, you need to activate these tags in the AWS Billing and Cost Management console, which cannot be done directly through Pulumi. Once they are activated, AWS will start including cost allocation tag information in your CUR data, typically within 24 hours of activation.

Using the combination of Pulumi with AWS services like SageMaker and Cost Explorer, you can automate the provisioning of your ML infrastructure and the cost allocation tags needed for precise tracking of your costs.