Accelerate and Improve Machine Learning with Connected Graph Databases.
Today's Brute Force methods of AI are not sustainable for future gains. Adding thousands of GPU's and TB of Memory at the problem are not cost efficient or scalable for complex solutions.
Knowledge Graphs greatly improve on the functionality of relational database for the storage of interrelated data. Relationships between data can be calculated far more quickly and with less compute power overhead.
GraphAI works by replacing database table joins with graph queries.
Sparce Matrices and relationships are replaced with graph structures.
Graphs are built to attempt to form a 1:1 relationship of the real world.
Agents/Questions are built to traverse the Graphs for Solutions/Answers.
Traditional Machine Learning Methods can be used to build weighted graph database(s) and combined to solve highly complex problems.
Random walks through the Graph Database can be used to train neural networks.
GraphAI is ideal for:
- Real-Time recommendations
- Supply Chain Logistics
- Patient Modeling
- Dynamic Pricing Models
- Solving Complex interdependent problems
- Constantly changing requirements and data
- Growing data-sets
- Identify your Use Case
- Inventory and Organize Relevant Data
- Map relationships across the Data
- Implement Use case(s) via Agents