An ML team runs a SageMaker Pipeline dozens of times per day during model development. Each execution launches a Training step on the same ml.g5.xlarge instance type, but the team notices that most of the total pipeline runtime is consumed by repeated instance provisioning and container startup between iterations. The datasets and hyperparameters change on nearly every run, so step caching provides no benefit. Which approach most directly reduces this provisioning overhead?