Data collected from individuals is the fuel that drives the modern AI revolution. Intense recent discussions have focused on how to provide individuals control over when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an example of this effort. Here we initiate a framework to study what to do when it is no longer permissible to deploy models derivative from certain individual user data. In particular, we formulate the problem of how to efficiently delete individual data from ML models that have been trained using this data. For many standard ML models, the only way to completely remove a person's data is to retrain the whole model from scratch on the remaining data. This is not practically feasible in many settings and we investigate ML models where data can be efficiently deleted