Cost Optimization and Governance

Production ML workloads on AWS span a wide surface area of billable services. A single fraud detection pipeline might ingest data through Amazon S3, transform it with AWS Glue, train models on SageMaker GPU instances, and serve predictions from real-time inference endpoints around the clock. Each of these stages generates costs independently, and without deliberate governance, monthly bills can grow 30–50% quarter over quarter due to overprovisioned endpoints, idle notebook instances, and resources that no one can attribute to a specific team or project. For the AWS Certified Machine Learning Engineer – Associate exam, you need to know which tools exist, when to use them, and why they matter across the cost optimization life cycle.

This lesson moves through four stages: gaining visibility with cost analysis tools, establishing accountability through resource tagging, rightsizing infrastructure with AWS Compute Optimizer and SageMaker Inference Recommender, and reducing unit costs through purchasing options like SageMaker Savings Plans and Managed Spot Training.

AWS cost analysis tools

AWS provides three complementary services that, when combined, deliver full cost observability across ML workloads. Each serves a distinct function in the governance workflow.

AWS Cost Explorer

AWS Cost Explorer visualizes historical and forecasted spend across all AWS services. You can filter by service (SageMaker, S3, Glue), linked account, or tag, and build custom reports that isolate ML-specific costs. Cost Explorer includes a built-in ML-based forecasting feature that projects future spend based on historical trends, which is useful for capacity planning before a new model deployment. The service updates data at least once every 24 hours, so it reflects near-real-time cost trends rather than instantaneous snapshots.

AWS Budgets

AWS Budgets allows you to set custom spending limits. When your SageMaker costs exceed a defined budget threshold, AWS ...