It incorporates a variety of cool base libraries like dirty_cat and transfomers, adds some of our own, and is heavier on text feature column support than most packages here. Think improving upon TopicBERT or dirty_cat for real text & multicolumn data and interactively visualizing, all in one line. We are currently adding end-to-end GPU support for that pipeline (the rest of our stack already supports that), primarily around cuml GPU data frame support.
It powers a lot of our new visual no-code AI layers and is driven by a bunch of enterprise projects in areas like cyber, fraud, social, misinfo, supply chain, & finance :) More niche, this is all feeding into our automatic graph ai (GNN) layers. Graph AI packages can't truly do graphs well until they can do node tables and edge tables well on their own!
As part of improving autoML in PyGraphistry, we have been a doing a lot of auto-feature-engineering and auto-clustering work for one-liners like:
g.nodes(pd.read_csv(...)).featurize().umap().plot()
It incorporates a variety of cool base libraries like dirty_cat and transfomers, adds some of our own, and is heavier on text feature column support than most packages here. Think improving upon TopicBERT or dirty_cat for real text & multicolumn data and interactively visualizing, all in one line. We are currently adding end-to-end GPU support for that pipeline (the rest of our stack already supports that), primarily around cuml GPU data frame support.
It powers a lot of our new visual no-code AI layers and is driven by a bunch of enterprise projects in areas like cyber, fraud, social, misinfo, supply chain, & finance :) More niche, this is all feeding into our automatic graph ai (GNN) layers. Graph AI packages can't truly do graphs well until they can do node tables and edge tables well on their own!