Graph-Powered Analytics And Machine Learning With TigerGraph
About the Paper
Early Release - Machine Learning Focused Chapters
With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.
You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Xinyu Chang, and Gaurav Deshpande from TigerGraph present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization.
- Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learning
- Learn how graph analytics and machine learning can deliver key business insights and outcomes
- Use five core categories of graph algorithms to drive advanced analytics and machine learning
- Deliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizen
- Discover insights from connected data through machine learning and advanced analytics
This is the early-release version of the book. It contains multiple chapters that will teach you how to combine machine learning with graph with hands-on exercises. You will receive additional chapters throughout 2021 to continue your learning and access to free tier on TigerGraph Cloud to perform exercises from the book.
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