Harnessing the Power of Graphs for AI and Machine Learning
Graph technologies are the basis for creating intelligent applications that allow for more accurate predictions and faster decisions.
What's inside?
To navigate today’s rapidly changing business landscape, enterprises need to maximize the value of their data to drive efficiency, agility, and innovation.
From accelerating analytics, artificial intelligence (AI), and machine learning projects to supporting next-generation data fabric architectures, knowledge graphs have emerged as a powerful solution for enterprises hungry for greater automation and intelligence.
The flexible, semantic nature of knowledge graphs makes them well-suited for managing and storing data from diverse, heterogeneous sources—data pipelines that continuously add new knowledge, connections, context, and inferences.
However, turning data into meaningful information remains a challenge for many organizations.
Data silos are still a huge problem, and legacy data management technologies and processes are not keeping up with the speed, scalability, and flexibility requirements of new workloads and use cases.
A knowledge graph can provide a central place to find data and understand it—offering a single source of truth.
Fill out the form for a better understanding of what's to gain by using knowledge graph for AI.