While executing a model training pipeline on Vertex AI, it has come to your attention that the evaluation step is encountering an out-of-memory error. Your current setup involves the use of TensorFlow Model Analysis (TFMA) within a standard Evaluator component of the TensorFlow Extended (TFX) pipeline for the evaluation process. Your objective is to address this issue and stabilize the pipeline's performance without compromising the quality of evaluation, all while keeping infrastructure overhead to a minimum. What course of action should you take?