Graph-Based Data Science: Hybrid AI meets data science process
A Talk by Paco Nathan
About this talk
Presentation Slides available here: https://derwen.ai/s/kcgh#qr<hr> <iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/bZlt9PjMl9s" title="YouTube video player" width="560"></iframe>
Paco's Masterclass sneak peak<hr>
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 (
pytorch), nor efficiently with popular data engineering infrastructure such as
This talk 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.
We'll show an example of how to build and leverage a personal knowledge graph using just a small amount of Python.
Categories covered by this talk
Known as a "player/coach", with core expertise in data science, natural language, machine learning, cloud computing; 38+ years tech industry experience, ranging from Bell Labs to early-stage start-ups.
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