Machine learning models depend on large troves of data to develop and improve their inference / prediction capabilities. These models generalize their abilities, but inevitably some of their training data is encoded or memorized within their trained parameters. This post explores a method of quantifying model memorization, factors that make models more prone to memorization, and provides some mitigations against memorization.