GartnerResearch_logo

3 Ways to Enhance AI With Graph Analytics and Machine Learning

Graph analytics and machine learning are two powerful tools that have the potential to greatly improve artificial intelligence (AI). By using graph analytics, we can analyze and understand complex relationships and patterns in data that may be difficult to uncover with traditional methods. This can be especially useful in areas such as social networks, fraud detection, and recommendation systems.

Machine learning, on the other hand, allows AI systems to learn from data and improve their performance over time. By combining machine learning with graph analytics, we can create more sophisticated AI models that can make better predictions and decisions.

 To discover more about how to enhance AI with Graph Analytics and Machine Learning, download the reprint.
            

Download the Research👇
gartnerWHITEPAPERV3-1

Graph Analytics & Machine Learning Market

According Gartner®:
"Data and analytics leaders should adopt graph data science, incorporating graph analytics, graph-based features and graph machine learning in their workflow. By 2025, Graph technologies will be use by 80% of data and analytics innovations, up from 20% in 2021, facilitating rapid decision making across the enterprise."

Attribution and disclaimers 

Gartner, 3 Ways to Enhance AI With Graph Analytics and Machine Learning, 18 August 2022, Afraz Jaffri.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Who is Perfect Memory?

Perfect Memory is a  high-end technology provider and software editor. Through our semantic AI composite technology, we help organisations to operate more efficiently and tackle their digital challenge with Knowledge Graph technologies that reactivate corporate memory, uncover hidden knowledge and reveal new sources of untapped value creation.We bring value wherever there is a need to create a common language and interoperability between heterogeneous data to understand what connects them and to learn from these crossings.

CONTACT US