TigerGraph, a California-based company, has launched a new machine learning capability explicitly designed to boost the development and improve the accuracy of data science models.
The graph database vendor, founded in 2012, announced the TigerGraph ML Workbench in a preview during the Graph + AI Summit, the spring edition of the biannual open conference hosted by TigerGraph, virtually.
The new capability will act as a platform for developing and deploying machine learning models with the latest graph technology. It works with leading third-party machine learning platforms.
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The platform by TigerGraph uses graph technology that enables data points within databases to simultaneously connect to multiple other data points, instead of just one data point at a time as in the case of a traditional relational database. Users can more easily discover relationships between data by connecting to multiple data points simultaneously. This results in speed and accuracy, days the vendor.
The tool is compatible with TigerGraph 3.2 and versions succeeding that. It can be deployed as either a fully managed cloud service or on-premises and will be available in June 2022.
More typical use cases for graph technology include the development of social media platforms and fraud detection, which rely heavily on discovering relationships.
TigerGraph ML Workbench aims to make data science, explicitly developing machine learning models, faster, easier, and more accurate. According to Victor Lee, vice-president of machine learning and AI at TigerGraph, Graph technology is boosting the analytics process and enabling users to reach insight and action more accurately than they could with relational databases.