Modern machine learning demands new approaches.
A powerful ML workflow requires more than picking the right
algorithms. You also need the right tools, technology, datasets and model to brew your secret ingredient: context.
In his book, Graph-Powered Machine Learning, Dr. Alessandro Negro
explores the new way of applying graph-powered machine learning to recommendation engines, fraud detection systems, natural language processing.
By making connections explicit, graphs harness the power of context to help you build more accurate, real-time machine learning models.
In this interview with the book’s author, you’ll learn more about: The role of graph technology in machine learning applications.How graphs provide better context to improve your ML understanding and workflow.
How graph data science enhances four of the most common recommendation techniques: content-based, collaborative filtering, session-based, and context-aware recommendations.
Data modeling considerations for graph-based recommendation engines.
How to approach designing a hybrid recommendation engine that incorporates multiple approaches.
Dr. Alessandro Negro, GraphAware
Amy Hodler, Neo4j
Hope to see you there, Amy Hodler