This chapter surveys the practical applications of conformal prediction and related methods across diverse domains. We examine how these techniques, which provide rigorous uncertainty quantification with minimal distributional assumptions, have been adapted to address real-world challenges in fields such as drug discovery, medical diagnostics, financial risk management, autonomous systems, natural language processing, and industrial engineering. Key developments include the use of conformal methods to enhance safety in autonomous driving, mitigate hallucinations in large language models, detect anomalies in data streams, and optimize maintenance schedules. We also review the growing ecosystem of open-source software that has facilitated adoption of these methods in industry. By providing guaranteed error control and calibrated probabilities, conformal prediction is enabling more reliable and trustworthy machine learning systems in high-stakes applications. This survey demonstrates how the theoretical foundations laid by Alexander Gammerman and colleagues have evolved into practical tools for building safer and more robust AI systems.