MLCOST04-BP12 Set up a budget and use resource tagging to track costs
Setting up budgets and implementing resource tagging for machine learning workloads provides clear visibility into your ML-related expenses and optimizes costs across your organization. By tracking costs effectively, you can make data-driven decisions about resource allocation and identify opportunities for cost optimization.
Desired outcome: You gain complete visibility into your machine learning costs across development, training, and production environments. You can track expenses by project, business unit, or environment, allowing for accurate cost allocation and forecasting. Through tagging and budgeting tools, you can proactively manage your ML spending, receive alerts before exceeding budgeted amounts, and make informed decisions about resource provisioning and termination.
Common anti-patterns:
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Running ML workloads without cost monitoring mechanisms in place.
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Using generic cost tracking that doesn't differentiate between ML projects or environments.
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Failing to tag ML resources consistently, making cost allocation difficult.
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Ignoring budget alerts or failing to take action when exceeding thresholds.
Benefits of establishing this best practice:
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Clear visibility into where ML spending occurs across your organization.
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Ability to accurately allocate costs to specific projects or business units.
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Early warning through alerts when costs exceed or are forecasted to exceed budgeted amounts.
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Improved governance and financial accountability for ML initiatives.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Cost management is a critical aspect of running machine learning workloads in the cloud. Without proper cost tracking and budget controls, ML expenses can quickly escalate due to compute-intensive training jobs, large storage requirements for datasets, and continuous inference endpoints. By implementing comprehensive budgeting and tagging strategies, you gain visibility and control over these costs.
AWS provides several tools that work together to track, analyze, and optimize your ML costs. AWS Budgets allows you to set custom budgets for your SageMaker AI resources, while AWS Cost Explorer provides visualization and analysis capabilities to understand spending patterns. Resource tagging serves as the foundation for detailed cost tracking, enabling you to categorize expenses by project, team, environment, or other dimension important to your organization.
For example, you might tag resources related to a fraud detection model with a Project tag value of FraudDetection and an Environment tag value of Production. This allows you to track the total cost of this specific ML use case across its components, from development notebooks to training jobs to deployment endpoints.
Implementation steps
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Set up AWS Budgets for ML cost tracking. Create customized budgets in AWS Budgets to monitor your Amazon SageMaker AI costs across development, training, and hosting. Configure the budget to track specific services (such as SageMaker AI) or specific tagged resources. Set thresholds for actual costs and forecasted costs to receive notifications before you exceed your budget. This gives you time to make adjustments to your resource usage if needed. Access your budgets through the AWS Budgets console
to track progress and make adjustments as necessary. -
Implement a tagging strategy for ML resources. Develop a consistent tagging strategy for all your ML resources. Define mandatory tags such as Project, BusinessUnit, Environment (dev/test/prod), and Owner. Document your tagging standards and verify that team members understand and follow these standards. Apply these tags to relevant resources, including Amazon SageMaker AI
notebook instances, training jobs, models, endpoints, and related resources like Amazon S3 buckets for dataset storage. -
Activate cost allocation tags. After implementing your tagging strategy, activate your tags as cost allocation tags in the AWS Billing and Cost Management console. Note that it may take up to 24 hours for newly activated tags to appear in your cost management tools. Once activated, you can use your tags to filter and group costs in AWS Cost Explorer and other cost reporting tools.
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Configure detailed cost analysis using AWS Cost Explorer. Use AWS Cost Explorer
to visualize and analyze your ML costs over time. Create custom reports that filter costs by specific tags (like Project or Environment) or by specific services like SageMaker AI. Set up regular reports to track spending trends, identify cost spikes, and understand usage patterns. Use the insights gained to optimize your resource allocation and scheduling for ML workloads. -
Create cost anomaly detection. Set up AWS Cost Anomaly Detection
to automatically identify unusual spending patterns in your ML workloads. Configure alerts to notify relevant stakeholders when anomalies are detected. This assists you in quickly identifying and addressing unexpected cost increases, which can happen with ML workloads due to extended training times or inefficient resource usage. -
Establish cost governance processes. Create clear processes for reviewing costs, responding to budget alerts, and making cost optimization decisions. Assign responsibility for cost monitoring to specific individuals or teams. Conduct regular cost reviews with stakeholders to discuss spending trends, identify optimization opportunities, and align ML resource usage with business priorities. Document cost-saving actions taken and their impact on the overall budget.
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Optimize ML resources based on cost data. Use the cost insights gained from your tagging and budgeting tools to optimize ML resource usage. Identify underutilized notebook instances that can be stopped when not in use. Select appropriate instance types based on workload requirements. Consider using Amazon SageMaker AI Managed Spot Training to reduce training costs by up to 90%. Implement auto-scaling for inference endpoints to match capacity with demand.
Resources
Related documents:
