Geostatistical Learning is a new branch of Geostatistics concerned with learning functions over geospatial domains (e.g. 2D maps, 3D subsurface models). The theory is being carefully implemented in the GeoStats.jl framework, which is an extensible framework for high-performance geostatistics in Julia. In this talk, I will illustrate how the framework can be used to learn functions over general unstructured meshes, and how this unique technology can help advance geoscientific work.