You are building a Vertex AI Pipeline using the Kubeflow Pipelines (KFP) v2 SDK. The pipeline has three components: a lightweight data validation step, a CPU-heavy feature engineering step, and a GPU-intensive model training step. During test runs, all steps are provisioned with the same default machine type, causing the training step to run out of memory while the validation step wastes expensive resources. You want each component to request appropriate compute resources without changing pipeline logic. What is the recommended approach?