The extrapolation of creep data to longer times is technically very important. The traditional way of extrapolating creep rupture data is to use time temperaturer parameters (TTPs). In this way data from several test temperatures are combined to a single master curve that can be used to assess rupture strengths at long times. Recently, there is much focus on machine learning techniques (neural networks, NNs). Both types of procedures can generate accurate results, but a detailed analysis is required. A good way to assess the quality of the results is to use the post assessment tests (PATs) developed by ECCC. Without such tests arbitrary results can be obtained. They are important for both TTPs and NNs. It has been shown that by putting requirements on the derivatives of the creep rupture curves, the PATs can more or less automatically be satisfied. In addition, the error in the extrapolated values should be estimated. Using the basic creep models presented in this book, prediction of rupture strength and ductility can be made in a safer way. It is demonstrated for Cu that accurate extrapolation of many order of magnitude in the creep rate can be made, which is never possible with empirical models.