Explore machine learning and resampling techniques for stochastic parameterization with memory in this 25-minute conference talk. Delve into the world of multiscale dynamical systems, such as the climate system, and discover how data-based methods using machine learning are revolutionizing the parameterization of unresolved processes. Learn about the advantages of stochastic parameterization over deterministic approaches in accounting for uncertainty in small-scale to large-scale process feedback. Examine recent work on constructing data-based stochastic parameterizations with memory through resampling techniques, including binning and neural networks for probabilistic classification. Gain insights into overcoming the curse of dimensionality in long memory scenarios and evaluate the performance of these approaches on various test problems.