Building a good scoring model is just the beginning. In the age of critical AI applications, understanding and quantifying uncertainty is as crucial as achieving high accuracy. This talk highlights conformal prediction as the definitive approach to both uncertainty quantification and probability calibration, two extremely important topics in Deep Learning and Machine Learning. We’ll explore its theoretical underpinnings, practical implementations using TorchCP, and transformative impact on safety-critical fields like healthcare, robotics, and NLP. Whether you're building predictive systems or deploying AI in high-stakes environments, this session will provide actionable insights to level up your modelling skills for robust decision-making.