3 Considerations for Adding Real-Time ML to Applications
Data that is ready for machine learning will be observable, supported by real-time infrastructure, and primarily processed with streaming technologies.
Dr. Denise Gosnell is a distinguished engineering lead at DataStax. She applies her experiences from within the graph and machine learning industries to drive more informed decisions with data. Most recently, she published the book The Practitioners Guide to Graph Data, which illustrates how to apply graph thinking to solve complex problems. Dr. Gosnell earned her Ph.D. in Computer Science from the University of Tennessee as an NSF Fellow. Her research coined the concept of "social fingerprinting" by applying graph algorithms to predict user identity from telecommunication interactions. She holds two patents on novel applications of graph technology within the healthcare industry.
Data that is ready for machine learning will be observable, supported by real-time infrastructure, and primarily processed with streaming technologies.
Follow these best practices for data lake management to ensure your organization can make the most of your investment.
The need for automated data pipelines is clear. What role will data scientists play in bringing them about?
Developing an enterprise-ready application that is based on machine learning requires multiple types of developers.
Cloud optimization could offer the best method for reducing costs according to a new report.