As an example of how the code-portability challenge can be met for an originally developed AI application which is different from deep-learning, we outline the porting of an implementation of the sureindependence screening and sparsifying operator (SISSO) to GPUs using Kokkos. SISSO is a combination of symbolic regression and compressed sensing. It first generates a list of up to trillions of analytical expressions from an initial set of primary features and mathematical operators. It then uses an ℓ0 regularised least squares regression to find the best low-dimensional linear model from the generated expressions. In preparation for (pre-)exascale computing, we converted the most computationally intensive components of SISSO, i.e. expression generation and ℓ0 regularisation, in our initial MPI+OpenMP code to a MPI+OpenMP+Kokkos implementation, in order to demonstrate scalability and portability on exascale-ready HPC platforms.