The overall workflow (see Figure ) comprises three major steps: i) Initialization of Training Data (Figure ), ii) ML Model Building via Human-in-the-Loop Approach Once the ML model reached optimal performance, it was deployed in the Cloud Lab to autonomously screen HPLC experiments in real time. The purpose of the trained model is to screen and identify affected HPLC traces autonomously. Two prospective validation steps-one at the experiment level and one at the instrument level-were performed to ensure that the model's predictions align with real-world scenarios, thereby confirming its reliability and effectiveness.