ArangoDB for Machine Learning

Traditional machine learning misses connections and relationships between data points, which is where graph machine learn shines. But graph ML is currently only accessible to large enterprises with dedicated teams of data scientists. ArangoGraphML makes it easier to use graph ML to gain deeper insights from your data.

Node classification is at the heart of many machine learning tasks. For example, is a node representing a seller account in an online marketplace classified as fraudulent (selling counterfeit goods) or not? ArangoGraphML makes it easier for data scientists to perform this core part of their job via an intuitive user interface, or an API call from other machine learning tools that they use.

To help data science teams focus on high-value tasks, ArangoGraphML includes MLOps to simplify the process of creating machine learning pipelines. Features include model training and management, hyperparameter optimization, metadata, lineage tracking of models and other artifacts, metrics, and dataset management.

To uncover machine learning insights faster, ArangoGraphML runs on GPUs (graphics processing units). GPUs are silicon chips that can run computation tasks in parallel and therefore much faster than traditional CPUs, providing increased performance when analyzing large, distributed graphs.