TigerGraph Puts State-of-the-Art Graph Neural Networks within Reach

Machine learning (ML) has revolutionized various industries and aspects of daily life. However, the performance plateau poses a challenge for ML algorithms due to diminishing returns on accuracy improvements. Graph machine learning, such as graph neural networks, can boost algorithm accuracy and performance by incorporating connections between data points. TigerGraph is an example of a graph database that enhances ML outcomes by providing additional features and contextual information. The TigerGraph Machine Learning Workbench allows businesses to improve their ML models without building graph technology from scratch. By leveraging connected data, companies can overcome the performance plateau and optimize their machine learning processes.