Neo4j announced version 2.0 of its Graph Data Science software, containing 65 new graph algorithms, better data import capabilities and support for MLOps
Graph database maker Neo4j introduced a version of its Aura managed service aimed at data scientists. The new offering complements an existing managed version of its core graph database the company rolled out early last year. Neo4j also announced version 2.0 of its Graph Data Science software, containing 65 new graph algorithms, better data import capabilities and support for MLOps, which is a machine learning engineering discipline focused on speed and continuous development.
Neo4j Graph Data Science is designed to make it easy for data scientists to achieve greater predictive accuracy with comprehensive graph analysis techniques. Users can improve models through a library of graph algorithms, ML pipelines, and data science methods. Neo4j Graph Data Science has been widely adopted and is trusted to perform at scale, easily handling hundreds of billions of nodes and relationships.
“Data science is a growing segment of our enterprise customers that made up about 30% of new customers last quarter. About 20% of all our customers are using some version of the company’s product for data science. Now they can use Python in a Jupyter notebook and not have to worry about syntax and integration with their workflows,” said Alicia Frame, Senior Director, Graph Data Science, Neo4j.
AuraDS has all the usual managed service features, including automated patching and backup and pay-as-you-go pricing. The new offering also allows users to take a snapshot of instance models and in-memory graphs with one click and pause instances without saving them.
Managing pipelines is made easier through the addition of a pipeline catalogue, a new unified syntax for model configuration, training and application, and support for random forest models. There’s also native support for popular data science algorithms like Breadth-First Search, Depth First Search, K-Nearest Neighbors and Delta Stepping. This version also features native support for Python, a popular data science development language. This eliminates the need to write configuration scripts and application program interface code that was previously necessary.
