Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning.
However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spacy, pytorch), nor efficiently with popular data engineering infrastructure such as Spark, RAPIDS, Ray, Parquet, fsspect, etc.
In this podcast episode, Paco Nathan reviews kglab – an open source project that integrates most all of the above, and moreover provides ways to leverage disparate techniques in ways that complement each other, to produce Hybrid AI solutions for industry use cases.
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