The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free  prediction is laid out based on the notion of transforming a given set-up into one that is easier to work with, namely i.i.d.~or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under  minimal assumptions. Interestingly, model-free prediction in regression is a totally  automatic  technique that does not necessitate the search for an optimal data  transformation before model fitting.The resulting model-free predictive distributions and intervals are  compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained.