Graph Analytic Systems with Zachary Hanif - TWiML Talk #188
Published:Oct 8, 2018 19:49
•1 min read
•Practical AI
Analysis
This article is a summary of a podcast episode featuring Zachary Hanif, Director of Machine Learning at Capital One's Center for Machine Learning. The discussion focuses on graph analytics within the machine learning toolkit. The article highlights the importance of graph-based systems and their applications. It also touches upon different implementation methods, including graphical processing engines, which are particularly effective for large datasets. The article serves as an introduction to the topic, providing a high-level overview of graph analytics and its relevance in the field of machine learning.
Key Takeaways
- •The article discusses the role of graph analytics in the ML toolkit.
- •It highlights important application areas for graph-based systems.
- •It mentions graphical processing engines as a method for implementing graph analytics.
Reference
“Zach gives us an overview of the different ways to implement graph analytics, including what he calls graphical processing engines which excel at handling large datasets, & much m”