Graph-Based Data Science: Hybrid AI meets data science process | Paco Nathan | Learning w Machines

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. 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. -- Paco Nathan. Managing Partner, Derwen, Inc. 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. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise. -- Welcome to Connected Data London's #ThrowbackThursday Every Thursday at 3pm GMT, we are releasing gems from our vault on #YouTube Tune in and learn from leaders and innovators; subscribe to our channel and watch premieres as they are released! #AI #MachineLearning #OpenSource #KnowledgeGraph #Python #DataScience