UMAP has largely replaced t-SNE in our toolkit as one of our top go-to viz pipelines. Unlike most examples out there, we post-process with k-nn to expose the gr

UMAP has largely replaced t-SNE in our toolkit as one of our top go-to viz pipelines. Unlike most examples out there, we post-process with k-nn to expose the graph of correlations over arbitrary data sets -- bank accounts fraud scores, cancer protein mutations, twitter bots, malware files, etc. -- and then investigate. Algorithms like UMAP figure out this connectivity anyways (see also: TDA), and useful for guiding subsequent explorations. If you're doing an interactive analysis, like looking at data in a Jupyter notebook, super powerful to expose that inferred connectivity and make it interactive (on-the-fly filtering, clustering, etc.) on it. Tool-wise, we do it in a few lines over tables with many rows/columns via end-to-end GPU acceleration using https://www.RAPIDS.ai https://www.RAPIDS.ai (GPU dataframes + UMAP) + Graphistry (GPU viz, which we make).